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Smart Grid System Report March 2011 2010 Smart Grid System Report Report to Congress February 2012 ENERGY United States Department of Energy Washington, DC 20585 U.S. Department of

Smart Grid System Report 2010 Smart Grid System …energy.gov/sites/prod/files/2010 Smart Grid System Report.pdfThe 2010 Smart Grid System Report (SGSR) to Congress explores the current

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Smart Grid System Report

March 2011

2010 Smart

Grid System

Report Report to Congress

February 2012

ENERGY

United States Department of Energy

Washington, DC 20585

U.S. Department of

Message from the Assistant Secretary

February 2012

The Department of Energy is responding to Section 1302 of Title XIII of the Energy

Independence and Security Act (EISA), which directs the Secretary of Energy to report to

Congress concerning the status of smart grid deployments nationwide and any regulatory or

government barriers to continued deployment. This document is the second installment of this

report to Congress, which is to be updated biennially.

The 2010 Smart Grid System Report (SGSR) to Congress explores the current status of smart

grid development, its future prospects, and the technical and financial obstacles to progress. It

also outlines the scope of a smart grid, assesses the stakeholder landscape and provides several

recommendations for future reports.

A smart grid uses digital technology to improve the reliability, security, and efficiency of the

electricity system: from large generation through the delivery systems to electricity consumers

and a growing number of distributed generation and storage resources. The information

networks that are transforming our economy in other areas are also being applied to

applications for dynamic optimization of electricity system operations, maintenance, and

planning. Resources and services that had been separately managed are now being integrated

and re-bundled as we address traditional problems in new ways, adapt the system to tackle

new challenges, and discover new benefits that have transformational potential.

The report concludes that near-term progress in smart grid deployments has been

significant due primarily to the investments made under the American Recovery and

Reinvestment Act (ARRA) of 2009. The far-reaching impacts of ARRA include: funding a $2.4

billion program designed to establish 30 manufacturing facilities for electric vehicle batteries

and components, funding the deployment of 877 phasor measurement units, $812.6 million in

federal grant awards for advanced metering infrastructure deployments, and the provision of

$7.2 billion to expand broadband access and adoption. The report also highlights other

significant developments occurring since the last SGSR that have resulted in progress toward

achieving a smart grid. Pursuant to statutory requirements, this report is being provided to the

following Members of Congress:

• The Honorable Joseph Biden

President of the Senate

• The Honorable John Boehner

Speaker of the House of Representatives

• The Honorable Daniel K. Inouye

Chairman, Senate Committee on Appropriations

• The Honorable Thad Cochran

Ranking Member, Senate Committee on Appropriations

• The Honorable Hal Rogers

Chairman, House Committee on Appropriations

• The Honorable Norm Dicks

Ranking Member, House Committee on Appropriations

• The Honorable Fred Upton

Chairman, House Committee on Energy and Commerce

• The Honorable Henry A. Waxman

Ranking Member, House Committee on Energy and Commerce

• The Honorable Edward Whitfield

Chairman, Subcommittee on Energy and Power

House Committee on Energy and Commerce

• The Honorable Bobby Rush

Ranking Member, Subcommittee on Energy and Power

House Committee on Energy and Commerce

• The Honorable Jeff Bingaman

Chairman, Senate Committee on Energy and Natural Resources

• The Honorable Lisa Murkowski

Ranking Member, Senate Committee on Energy and Natural Resources

If you have any questions or need additional information, please contact me or Mr. Robert

Tuttle, Office of Congressional and Intergovernmental Affairs, at (202) 586-5450.

Sincerely,

Patricia A. Hoffman, Assistant Secretary

Office of Electricity Delivery and Energy Reliability

Department of Energy February 2012

Smart Grid System Report | Page v

Executive Summary

Section 1302 of Title XIII of the Energy Independence and Security Act (EISA) of 2007 directs

the Secretary of Energy to “…report to Congress concerning the status of smart grid

deployments nationwide and any regulatory or government barriers to continued deployment.”

This document is the second installment of this biennial report.

A smart grid uses digital technology to improve the reliability, security, and efficiency of the

electricity system, from large generation through the delivery systems to electricity consumers.

Smart grid deployment covers a broad array of electricity system capabilities and services

enabled through pervasive communication and information technology, with the objective of

improving reliability, operating efficiency, resiliency to threats, and our impact on the

environment.

Near-term progress in smart grid deployments has been significant due primarily to the

investments made under the American Recovery and Reinvestment Act (ARRA) of 2009,

including:

• providing $4.5 billion in awards for all programs described under Title XIII (111 USC 405)

• funding a $2.4 billion program designed to establish 30 manufacturing facilities for electric

vehicle batteries and components

• funding the deployment of 877 phasor measurement units

• providing $812.6 million in federal grant awards for advanced metering infrastructure

deployments

• providing $7.2 billion to expand broadband access and adoption

Recent progress toward achieving a smart grid also includes the following:

• There are now 29 states that have renewable portfolio standards.

• Distributed resource interconnection policies have been either implemented or expanded in

14 states since 2008, thus promoting the advancement of distributed generation

technologies.

• Incentives to purchase and own electric vehicles and plug-in hybrid electric vehicles are

either planned or provided in 21 states.

• The National Institute of Standards and Technology published the first release of the

framework for smart grid interoperability standards and guidelines for smart grid cyber

security.

With the aforementioned progress noted, significant challenges to realizing smart grid

capabilities persist. Foremost among these are the challenges tied to the value proposition and

the capital required to purchase the new technologies envisioned for communicating

Department of Energy February 2012

Smart Grid System Report | Page vi

information between end-users, energy providers, and distribution and transmission providers.

These and other challenges are explored in this report, as are recommendations for enhancing

future smart grid system reports.

Department of Energy February 2012

Smart Grid System Report | Page vii

Acronyms and Abbreviations

AC Auditably Compliant, alternating current

ACES American Clean Energy and Security Act

AEO Annual Energy Outlook

AEP American Electric Power

AMI advanced metering infrastructure

AMR automated meter reading

ARPA-E Advanced Research Projects Agency-Energy

ARRA American Recovery and Reinvestment Act

ATVM Advanced Technology Vehicle Manufacturing

BA balancing authority

BAT best available technology

BAU business-as-usual

BCCS Base Case Coordination System

BES bulk electricity system

BEV battery electric vehicle

BGE Baltimore Gas and Electric

BPA Bonneville Power Administration

CAES compressed-air energy storage system

CAIDI Customer Average Interruption Duration Index

CAISO California Independent System Operator

CAPEX capital expenditures

CBL customer baseline load

CCET Center for the Commercialization of Electric Technologies

CCS carbon capture and sequestration

CEC California Energy Commission

CERTS Consortium for Electric Reliability Technology Solutions

CES community energy storage

CHP combined heat and power

CIM common information model

CIP critical infrastructure protection

CL&P Connecticut Light and Power

Department of Energy February 2012

Smart Grid System Report | Page viii

CMM Capability Maturity Model

CMMI Capability Maturity Model for Software Integration

CO2 carbon dioxide

CPP critical-peak pricing

DER distributed energy resources

DG distributed generation

DHS Department of Homeland Security

DLC direct load control

DLR dynamic line ratings

DMS distribution management system

DoD United States Department of Defense

DOE United States Department of Energy

DOE-OE United States Department of Energy Office of Electricity Delivery and

Energy Reliability

DSIRE Database of State Incentives for Renewable Energy

DSM demand-side management

DTCR Dynamic Thermal Circuit Rating

DTE DTE Energy Company

EEI Edison Electric Institute

EHV extra-high voltage

EIA Energy Information Administration

EIPP Eastern Interconnection Phasor Pilot

EISA Energy Independence and Security Act of 2007

EMS energy management system

EPA United States Environmental Protection Agency

EPACT 2005 Energy Policy Act of 2005

EPRI Electric Power Research Institute

ERCOT Electric Reliability Council of Texas

EV electric vehicle

FCC Federal Communications Commission

FERC Federal Energy Regulatory Commission

FRCC Florida Reliability Coordinating Council

FY fiscal year

GE General Electric Company

Department of Energy February 2012

Smart Grid System Report | Page ix

GWAC DOE’s GridWise® Architecture Council

GWh gigawatt-hours

HAN home area network

HECO Hawaiian Electric Company, Inc.

HVAC Heating, Ventilation, & Air Conditioning

IED intelligent electronic device

IEEE Institute of Electrical and Electronics Engineers

INL Idaho National Laboratory

IOU investor-owned utility

IREC Interstate Renewable Energy Council

ISO independent system operator

ISO-NE Independent System Operator – New England

IT information technology

KCP&L Kansas City Power and Light

kW kilowatt

kWh kilowatt-hour

LBNL Lawrence Berkley National Laboratory

LG&E Louisville Gas and Electric

LRAM lost revenue adjustment mechanism

LUF line under-frequency

MAIFI Momentary Average Interruption Frequency Index

MAPP Mid-Atlantic Power Pathway

MRO Midwest Reliability Organization

MSRP manufacturer’s suggested retail price

MW megawatts

MWh megawatt-hours

NARUC National Association of Regulatory Utility Commissioners

NASPI North American SynchroPhasor Initiative

NEHTA National E-Health Transition Authority (Australia)

NEMS National Energy Modeling System

NERC North American Electric Reliability Corporation

NESC National Electric Safety Code

NETL National Energy Technology Laboratory

NIST National Institute of Standards and Technology

Department of Energy February 2012

Smart Grid System Report | Page x

NNEC Network for New Energy Choices

NPCC Northeast Power Coordinating Council

NRDC Natural Resources Defense Council

NSF National Science Foundation

NTIA National Telecommunications and Information Administration

NYISO New York Independent System Operator

OEM original equipment manufacturer

ORNL Oak Ridge National Laboratory

OWL Web Ontology Language

PDC phasor data concentrators

PG&E Pacific Gas and Electric Company

PHEV plug-in hybrid electric vehicles

PJM PJM Interconnection, Inc.

PM particulate matter

PMU phasor measurement units

PNNL Pacific Northwest National Laboratory

PQ power quality

PSC public service commission

PTC production tax credit

PUC public utility commission

PUCT Public Utility Commission of Texas

PV photovoltaic systems

R&D research and development

RC reliability coordinator

RDC Resource Dynamics Corporation

RDF Resource Description Framework

RES renewable energy source

RFC Reliability First Corporation

RMS root-mean square

ROI return on investment

RPS renewable portfolio standards

RTEP regional transmission expansion plan

RTO regional transmission operator

RTP real-time pricing

Department of Energy February 2012

Smart Grid System Report | Page xi

RUS Rural Utility Service

SAIDI System Average Interruption Duration Index

SAIFI System Average Interruption Frequency Index

SAR Standard Authorization Request

SCADA Supervisory Control and Data Acquisition

SCE Southern California Edison

SEI Software Engineering Institute

SERC Southeast Reliability Corporation

SGIG Smart Grid Investment Grant

SGIMM Smart Grid Interoperability Maturity Model

SGIP Smart Grid Investment Program, Self-Generation Incentive Program

SGSR Smart Grid System Report

SPP Southwest Power Pool

SUV sport utility vehicle

T&D transmission and distribution

TB terabytes

TLR transmission loading relief

TOP transmission operators

TOU time-of-use pricing

TRANSCO transmission-only companies

TRE Texas Regional Entity

TVA Tennessee Valley Authority

TWh terawatt hours, trillion watt hours

UMTRI University of Michigan Transportation Research Institute

U.S. United States of America

USDA United States Department of Agriculture

V2G vehicle-to-grid

VAR volt-amps reactive

VMT vehicle miles traveled

WAMS wide area measurement system

WECC Western Electricity Coordinating Council

WISP Western Interconnection Synchrophasor Program

Department of Energy February 2012

Smart Grid System Report | Page xiii

Table of Contents

Message from the Assistant Secretary ...................................................................... iii

Executive Summary ............................................................................................................ v

Acronyms and Abbreviations ...................................................................................... vii

1.0 Introduction .................................................................................................................. 1

1.1 Objectives ............................................................................................................... 1

1.2 Scope of a Smart Grid ........................................................................................ 2

1.3 Stakeholder Landscape .................................................................................... 4

1.4 Regional Influences ............................................................................................ 5

1.5 What’s New in this Report .............................................................................. 7

1.6 About This Document ....................................................................................... 9

2.0 Deployment Metrics and Measurements ........................................................ 11

2.1 Smart Grid Metrics ........................................................................................... 11

2.2 Smart Grid Characteristics ............................................................................ 16

2.3 Mapping Metrics to Characteristics .......................................................... 17

3.0 Deployment Trends and Projections ................................................................ 19

3.1 Enables Informed Participation by Customers .................................... 19

3.1.1 Grid-Enabled Bi-Directional Communication and Energy

Flows ............................................................................................................ 20

3.1.2 Managing Supply and Demand ......................................................... 26

3.2 Accommodating All Generation and Storage Options ....................... 28

3.2.1 Distributed Generation and Storage .............................................. 30

3.2.2 Standard Distributed-Resource Connection Policy ................. 31

3.3 Enables New Products, Services, and Markets ..................................... 34

3.3.1 Enabling New Products and Services ............................................ 36

3.3.2 Enabling New Markets ......................................................................... 39

3.4 Provides Power Quality for the Range of Needs .................................. 47

The Cost of Poor Power Quality .................................................................. 48

Smart Grid Solutions to Power Quality Issues ...................................... 49

3.5 Optimizing Asset Utilization & Operating Efficiency ......................... 52

Department of Energy February 2012

Smart Grid System Report | Page xiv

3.5.1 Bulk Generation ...................................................................................... 53

3.5.2 Delivery Infrastructure ........................................................................ 55

3.5.3 Distributed Energy Resources .......................................................... 57

3.5.4 Overall System Efficiency .................................................................... 58

3.6 Operates Resiliently to Disturbances, Attacks, & Natural

Disasters ............................................................................................................... 59

3.6.1 Area, Regional, National Coordination .......................................... 60

3.6.2 DER Response Metrics ......................................................................... 62

3.6.3 Delivery Infrastructure Metrics ....................................................... 63

3.6.4 Secure Information Networks .......................................................... 67

4.0 Challenges to Deployment .................................................................................... 71

4.1 Technical Challenges ....................................................................................... 72

4.2 Business and Financial Challenges ............................................................ 74

5.0 Recommendations for Future Reports ............................................................ 79

6.0 Conclusions.................................................................................................................. 83

6.1 Progress towards Realizing the Characteristics of the Modern

Grid ......................................................................................................................... 83

6.2 Challenges to Smart Grid Deployments ................................................... 86

6.3 Recommendations for Future Reports .................................................... 87

7.0 References .................................................................................................................... 89

Figures

1.1 Scope of Smart Grid Concerns ................................................................................. 2

1.2 Stakeholder Landscape .............................................................................................. 5

1.3 NERC Region Representation Map ........................................................................ 6

1.4 EPA eGRID Subregion Representational Map .................................................. 6

3.1 Overview of AMI Interface ........................................................................................ 21

3.2 National Historic Demand-Response and Load-Management Peak

Reduction as

a Percentage of Summer Net Capacity................................................................. 26

Department of Energy February 2012

Smart Grid System Report | Page xv

3.3 2030 Forecast Demand Response and Energy Efficiency Peak

Demand Offsets

by Sector ........................................................................................................................... 28

3.4 Yearly Installed DG Capacity by Technology Type ......................................... 30

3.5 State Interconnection Standards ........................................................................... 33

3.6 Favorability of State Interconnection Standards According to IREC

and NNEC ......................................................................................................................... 34

3.7 ARRA and Non-ARRA Smart Grid Investment Grant and

Demonstration Projects ............................................................................................. 36

3.8 PHEV Market Penetration Scenarios .................................................................... 39

3.9 Status of States with Decoupling or Lost Revenue Adjustment

Mechanisms .................................................................................................................... 41

3.10 Improved Performance Incentive Programs .................................................... 42

3.11 Venture Capital Funding of Smart Grid Startups ............................................ 43

3.12 Venture Capital Spending by Company Type ................................................... 44

3.13 Interoperability Categories ...................................................................................... 47

3.14 Trends in Renewables as a Percent of Total Net Generation..................... 52

3.15 Measured and Predicted Peak Summer, Peak Winter, and Yearly

Average

Generation Capacity Factors in the U.S ............................................................... 53

3.16 Generation Efficiency for Various Fossil Fuel Sources over Time ........... 54

3.17 Electricity Flow Diagram 2009 ............................................................................... 58

3.18 Combined Transmission and Distribution Efficiency over Time ............. 59

3.19 Current Installations of EMS/SCADA/DMS Systems by Type ................... 61

3.20 Number of PMUs Installed ........................................................................................ 62

3.21 US Summer Peak Reserve Margin Forecast ...................................................... 65

3.22 AMI Market Activity Growth .................................................................................... 66

3.23 Deemed Date Trend for Active and Closed Violations .................................. 69

Department of Energy February 2012

Smart Grid System Report | Page xvi

Tables

2.1 Summary of Smart Grid Metrics and Status ...................................................... 14

2.2 Smart Grid Characteristics ....................................................................................... 16

2.3 Map of Metrics to Smart Grid Characteristics .................................................. 18

3.1 Installed and Planned Smart Meters .................................................................... 22

3.2 Number of Entities Offering and Customers Served by Dynamic

Pricing Tariffs ................................................................................................................. 23

3.3 Yearly Installed DG Capacity by Technology Type ......................................... 31

3.4 Favorability Scoring Categories ............................................................................. 33

3.5 EV and PHEV Market Penetration ......................................................................... 38

3.6 Estimated Average Electricity Customer Interruption Cost Based on

U.S. 2008

Dollars by Customer Type and Duration ............................................................ 49

3.7 Federally Funded Micro Grid Projects ................................................................. 50

3.8 Measured and Projected Peak Demands and Generation Capacities

for Recent

Years in the U.S. and Calculated Average Capacity Factors ........................ 54

3.9 Rationale for Change in Investment ..................................................................... 55

3.10 Entities Offering Load-Management and Demand-Response

Programs .......................................................................................................................... 57

3.11 Regional Statistics on SAIDI, SAIFI and MAIFI 2006 ..................................... 64

3.12 Summary of the NERC Critical Infrastructure Protection Standards

CIP 002-009 .................................................................................................................... 68

3.13 Summary of the NERC Critical Infrastructure Protection Standards

CIP 010-011 .................................................................................................................... 68

3.14 Security Question from Electricity Service Provider Interviews ............. 69

Department of Energy February 2012

Smart Grid System Report| Page 1

1.0 Introduction

Section 1302 of Title XIII of the Energy Independence and Security Act of 2007 (EISA) directs

the Secretary of Energy to, “…report to Congress concerning the status of smart grid

deployments nationwide and any regulatory or government barriers to continued deployment”

(110 USC 1302). The first Smart Grid System Report (SGSR) was published in July 2009. This

document represents the second installment of this report to Congress, which is to be updated

biennially.

1.1 Objectives

The objective of Title XIII is to support the advancement of the Nation’s electricity system,

to maintain a reliable and secure infrastructure that can meet future load growth and achieve

the characteristics of a smart grid. The SGSR is to provide the current status of smart grid

development, the prospects for its future, and the obstacles to progress. In addition to

providing the state of smart grid deployments, the legislation includes the following

requirements and recommendations:

• Report the prospects of smart grid development, including costs and obstacles.

• Identify regulatory or government barriers.

• May provide recommendations for state and federal policies or actions.

• Take a regional perspective.

The first SGSR set the framework for future reports, as originally defined in Section 1302 of

Title XIII of EISA. This report, while retaining the original framework, goes into greater detail by

expanding the number of metrics explored in the report and using the baseline established in

the 2009 SGSR to update smart grid related progress. Figure 1.1 provides a pictorial view of the

many elements of the electricity system touched by smart grid concerns. The 21 metrics

evaluated in this report touch every element identified in the figure, from the accommodation

of all generation and energy options to the integration of end-user equipment, including

electric vehicles (EVs), smart appliances, and distributed generators.

Department of Energy February 2012

Smart Grid System Report| Page 2

Figure 1.1. Scope of Smart Grid Concerns

1.2 Scope of a Smart Grid

A smart grid uses digital technology to improve the reliability, security, and efficiency of the

electricity system. Due to the vast number of stakeholders and their various perspectives,

there has been debate on a definition of a smart grid that addresses the special emphasis

desired by each participant. This report retains the definition established in the 2009 SGSR, as

outlined in the remainder of this section.

The following areas arguably represent a reasonable partitioning of the electricity system

that covers the scope of smart grid concerns. To describe the progress being made in moving

toward a smart grid, one must also consider the interfaces between elements within each area

and the systemic issues that transcend areas. The areas of the electricity system that cover the

scope of a smart grid include the following:

Department of Energy February 2012

Smart Grid System Report| Page 3

• Area, regional and national coordination regimes: A series of interrelated, hierarchical

coordination functions exists for the economic and reliable operation of the electricity

system. These include balancing areas (BAs), independent system operators (ISOs), regional

transmission operators (RTOs), electricity market operations, and government emergency-

operation centers. Smart grid elements in this area include collecting measurements from

across the system to determine system state and health, and coordinating actions to

enhance economic efficiency, reliability, environmental compliance, or response to

disturbances.

• Distributed-energy resource technology: Arguably, the largest “new frontier” for smart grid

advancements, this area includes the integration of distributed generation (DG), storage,

and demand-side resources for participation in electricity system operation. Smart

appliances and EVs will become important components of this area, as are renewable-

generation components such as those derived from solar and local wind sources.

Aggregation mechanisms of distributed energy resources (DER) are also considered.

• Delivery transmission and distribution (T&D) infrastructure: this represents the delivery

part of the electricity system. Smart grid items at the transmission level include substation

automation; dynamic limits; relay coordination; and the associated sensing, communication,

and coordinated action. Distribution-level items include distribution automation (such as

feeder-load balancing, capacitor switching, and restoration) and advanced metering (such

as meter reading, remote-service enabling and disabling, and demand-response gateways).

• Central generation: generation plants already contain sophisticated plant automation

systems because the production cost savings provide clear signals for investment. While

technological progress in automation continues, the change is expected to be incremental

rather than transformational, and therefore, this area is not emphasized as part of this

report.

• Information networks and finance: information technology and pervasive communications

are cornerstones of a smart grid. Though the information network requirements

(capabilities and performance) will be different in different areas, their attributes tend to

transcend application areas. Examples include interoperability and the ease of integration

of automation components, as well as cyber security concerns. Information-technology-

related standards, methodologies, and tools also fall into this area. In addition, the

economic and investment environment for procuring smart-grid-related technology is an

important part of the discussion concerning implementation progress.

Section 1301 of EISA identifies characteristics of a smart grid. The National Energy

Technology Laboratory (NETL) Modern Grid Initiative provides a list of smart grid attributes in

What is the Smart Grid? (Miller 2008). These characteristics were used to help organize a

workshop sponsored by the U.S. Department of Energy (DOE) on “Implementing the Smart

Department of Energy February 2012

Smart Grid System Report| Page 4

Grid” (OE 2008). The results of that workshop were used to organize the reporting of smart

grid progress around six characteristics:

• Enabling Informed Participation by Customers

• Accommodating All Generation & Storage Options

• Enabling New Products, Services, & Markets

• Providing Power Quality for the Range of Needs

• Optimizing Asset Utilization & Operating Efficiency

• Operating Resiliently: Disturbances, Attacks, & Natural Disasters.

These characteristics were retained from the 2009 SGSR.

1.3 Stakeholder Landscape

Some aspect of the electricity system touches every person in the Nation. The smart grid

stakeholder landscape is complex, as demonstrated in Figure 1.2. The lines of distinction are

not always crisp, as corporations and other organizations can take on the characteristics and

responsibilities of multiple functions.

Stakeholders include the following:

• end users (consumers): industrial, commercial, residential

• electricity service retailers: regulated and unregulated electricity and other service

providers (including service and resource aggregators)

• distribution-service providers: generally electricity distribution utilities (public and private)

• transmission providers: generally electricity transmission owners and operators (public and

private)

• balancing authorities

• generation and demand wholesale-electricity traders/brokers

• wholesale market operators

• reliability coordinators including the North American Electric Reliability Corporation (NERC)

• products and services suppliers including information technology (IT) and communications

• local, state, and federal energy policymakers (regulators, legislators, executives, and related

offices)

• policy advocates (consumer groups, trade organizations, environmental advocates)

Department of Energy February 2012

Smart Grid System Report| Page 5

• standards organizations

• research organizations

• the financial community.

The major stakeholder groups are referenced throughout the report as appropriate to the

topic in question.

Figure 1.2. Stakeholder Landscape

1.4 Regional Influences

Different areas of the country have distinctions with regard to their generation resources,

their business economy, climate, topography, environmental concerns, and public policy.

These distinctions influence the picture for smart grid deployment in each region, provide

different incentives, and pose different obstacles for development. The major regions of the

country can be divided into the 10 NERC reliability regions (see Figure 1.3) (EPA 2008a). The

Environmental Protection Agency (EPA) further subdivides these into 26 sub-regions (see EPA

map, Figure 1.4), and each of these regions has its distinctive state and local governments.

Department of Energy February 2012

Smart Grid System Report| Page 6

Regional factors are woven into various aspects of the report, including the smart grid

deployment metrics, deployment attributes, trends, and obstacles. Discussion will target the

states and major NERC reliability regions.

Figure 1.3. NERC Region Representation Map

Figure 1.4. EPA eGRID Subregion Representational Map

Department of Energy February 2012

Smart Grid System Report| Page 7

1.5 What’s New in this Report

In May of 2010, the Pacific Northwest National Laboratory (PNNL) solicited stakeholder

feedback on the SGSR by hosting a series of webinars. At these webinars, which are discussed

in greater detail in Section 2.1 of this report, stakeholders provided input regarding the metrics

used in the SGSR. Based on the input received from those webinars, a small number of changes

were made to the 2010 SGSR:

• An additional metric [Metric 21–Grid-Connected Renewable Resources] has been added

that measures the generation levels associated with grid-connected renewable resources

and the displaced carbon dioxide (CO2) emissions attributed to their presence.

• Several metrics [Metrics 6–Load Served by Microgrids, 9–Grid-Responsive Non-Generating

Demand-Side Equipment, 16–Dynamic Line Ratings, , and 19–Open Architecture/Standards]

were deemed to be both nascent and not readily measurable given current data

constraints. In each case, the decision was made to keep the metric, monitor progress, and

re-evaluate it prior to including it in future SGSRs. Each metric addressed an area that was

viewed as having significant but as yet unrealized potential.

In addition, DOE’s Energy Advisory Committee and their Smart Grid Subcommittee were

consulted along with the inter-agency Smart Grid Task Force that includes representatives from

the National Institute of Standards and Technology (NIST), the Federal Energy Regulatory

Commission (FERC), the Department of Homeland Security (DHS), and EPA, among others.

Unlike the first SGSR, this report does include impacts related to the American Recovery and

Reinvestment Act of 2009 (ARRA). EISA provided incentives for electricity companies to

undertake smart grid investments. Section 1306 authorized the Secretary of the U.S. DOE to

establish the Smart Grid Investment Matching Grant Program (SGIG), which was designed to

provide reimbursement for up to 20 percent of an electricity service provider’s investment in

smart grid technologies. In 2009, ARRA altered sections 1306 and 1307 of Title XIII, providing

100 percent matching grants and designating $4.5 billion in awards for all programs described

under Title XIII (111 USC 405). To date, the SGIG program has awarded grants to 99 recipients,

including private companies, service providers, manufacturers, and cities, with total public-

private investment amounting to over $8 billion (DOE 2009a). The impacts of ARRA on the

metrics measured in the SGSR are far-reaching and include the following:

• ARRA funded the deployment of 877 phasor measurement units (PMUs), expanding the

prior nationwide network of 200 by more than 400 percent [Metric 2–Real-time System

Operations Data Sharing] (Overholt 2010).

• ARRA funded the Center for the Commercialization of Electric Technologies (CCET) Smart

Grid Demonstration Project, a demonstration-scale microgrid project in Texas [Metric 6–

Load Served by Microgrids].

Department of Energy February 2012

Smart Grid System Report| Page 8

• ARRA includes a $2.4 billion program designed to establish 30 manufacturing facilities for

electric vehicle batteries and components [Metric 8–Electric Vehicles and Plug-in Hybrid

Electric Vehicles]. This funding is in addition to the aforementioned $4.5 billion in awards

made under ARRA.

• Federal grant awards for advanced metering infrastructure (AMI) deployments under ARRA

total $812.6 million to date, with total project values reaching over $2 billion [Metric 12–

Advanced Meters] (DOE 2010a).

• ARRA included several provisions that will strengthen the nation’s broadband network.

ARRA provided $7.2 billion in funding to support grant and loan programs administered by

the National Telecommunications and Information Administration (NTIA) and the U.S.

Department of Agriculture’s (USDA’s) Rural Utilities Service (RUS) program, that are

designed to expand broadband access and adoption. ARRA also directs the Federal

Communications Commission (FCC) to develop and submit to Congress a National

Broadband Plan designed to measure progress toward the goal of providing access to

broadband capability across the U.S. Expanded access to broadband networks supports

several metrics [Metric 1–Dynamic Pricing, Metric 2–Real-time System Operations Data

Sharing, Metric 12–Advanced Meters] by enhancing the speed at which information can be

uploaded and shared between systems.

Other significant developments affecting the deployment trends reported in the 2010 SGSR

include:

• There are 29 states that now have renewable portfolio standards, which include specific

percentage goals to lower fossil fuel consumption by incorporating energy efficiency goals

and renewable energy generation. These standards have promoted smart grid deployment

by expanding or creating new policies for distributed resource interconnection, net

metering, energy efficiency programs, and regulatory recovery for smart grid investments.

• NIST released the first phase of a three-phase plan that aims to align smart grid standards.

The document, NIST Framework and Roadmap for Smart Grid Interoperability Standards

(NIST 2010), initially identified sixteen priority plan areas for smart grid standardization

including an initial plan for cyber security.

• NIST has identified the following five foundational families of standards, which are

fundamental to smart grid interoperability:

– International Electrotechnical Commission (IEC) 61970 and IEC 61968: Provides a

common information model (CIM) necessary for exchanges of data between devices and

networks, primarily in the transmission (IEC 61970) and distribution (IEC 61968)

domains

– IEC 61850: Facilitates substation automation and communication as well as

interoperability through a common data format

Department of Energy February 2012

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– IEC 60870-6: Facilitates exchanges of information between control centers

– IEC 62351: Addresses the cyber security of the communication protocols defined by the

preceding IEC standards.

• ARRA projects and data being collected for reporting and analysis will aid future SGSRs.

• The Energy Information Administration (EIA) was funded by ARRA to expand data collection

for the smart grid. DOE has been coordinating with the EIA to make sure that the expanded

information assists future SGSRs.

• Distributed resource interconnection policies have been either implemented or expanded in

14 states since 2008. As of June 2010, 39 states, Washington, D.C., and Puerto Rico have

adopted variations of an interconnection policy. The USDA’s RUS Loan Program requires all

existing borrowers to have a current and publicly available policy regarding the

interconnection of distributed resources. RUS borrowers (this does not include grant

recipients) serve customers in 44 states.

• Incentives for purchasing and owning EVs and plug-in hybrid electric vehicles (PHEVs) are

either planned or provided in 21 states.1 For example, Arizona lowers licensing fees for EVs,

and California offers rebates of up to $5,000 for battery electric vehicles (BEVs), $3,000 for

PHEVs, and $1,500 for electric motorcycles. Oregon recently put $5,000 tax credits in place

to offset conversion or purchase costs for PHEVs, and allows $1,500 tax credits for BEVs.

These incentives are in addition to federal tax credits of $2,500 to $7,500 for EVs and

PHEVs, depending on battery size.

1.6 About This Document

The SGSR is organized into a main body and two supporting appendices. The main body

discusses the metrics chosen to provide insight into the progress of smart grid deployment

nationally. The measurements resulting from research into the metrics are used to convey the

state of smart grid progress according to six characteristics derived from the NETL Modern Grid

Initiative’s work in this area and discussions at the DOE Smart Grid Implementation Workshop.

The main body of the report also summarizes the barriers to smart grid deployment, including

technical, business, and financial challenges, and concludes with a set of recommendations for

improving future SGSRs. Appendix A presents a discussion of each of the metrics chosen to

help measure the progress of smart grid deployment. Appendix B summarizes the results of

interviews with electricity service providers chosen to represent a cross section of the nation in

terms of size, location, and type of organization (e.g., public or private company, rural electric

cooperative).

1 The PHEV is a hybrid electric vehicle with batteries that can be recharged when plugged into an electric wall

outlet and an internal combustion engine that can be activated when batteries require recharging.

Department of Energy February 2012

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Finally, DOE conducts active R&D programs on many grid-related technologies, including

predictive computational modeling, power electronics, grid-scale energy storage systems, and

energy systems cybersecurity. Similarly, DOE conducts active R&D programs on electric-

generation and -consumption technologies, such as solar PV, hydropower, electric vehicles, and

energy-efficient appliances. While this report addresses metrics related to the deployment of

many of these technologies in the US energy system infrastructure, it does not include a

discussion of DOE technology R&D programs related to the electric grid.

Department of Energy | February 2012

Smart Grid System Report | Page 11

2.0 Deployment Metrics and Measurements

The scope of smart grid functionality extends throughout the electricity system and its

supply chain. To measure the status of smart grid deployments, multiple metrics were chosen

as indicators for examining smart grid progress. Although these metrics do not

comprehensively cover all aspects of a smart grid, they were chosen to address a balance of

coverage in significant functional areas and to support the communication of its status through

a set of smart grid attributes that have been formed through workshop engagements with

industry.

2.1 Smart Grid Metrics

On June 19-20, 2008, the U.S. Department of Energy brought together 140 experts,

representing the various smart grid stakeholder groups, at a workshop in Washington, DC. The

objective of the workshop was to identify a set of metrics for measuring progress toward

implementation of smart grid technologies, practices, and services. Breakout sessions for the

workshop were organized around seven major smart grid characteristics as developed through

another set of industry workshops sponsored by the NETL Modern Grid Strategy (Miller 2008).

The results of the workshop document over 50 metrics for measuring smart grid progress

(DOE 2008). Having balanced participation across the diverse electricity system stakeholders is

important for deriving appropriate metrics and was an important objective for selecting

individuals to invite to the workshop.

The workshop described two types of metrics: build metrics that describe attributes that

are built in support of smart grid capabilities, and value metrics that describe the value that

may be derived from achieving a smart grid. While build metrics tend to be easily quantifiable,

value metrics can be influenced by many developments and therefore generally require more

qualifying discussion. Both types are important to describe the status of smart grid

implementation.

After reviewing the workshop results, distilling the recorded ideas and augmenting them

with additional insights provided by the research team, 20 metrics were defined for the 2009

SGSR. In re-examining the original metrics, the research team viewed the 2010 SGSR as an

opportunity to slightly revise rather than overhaul the original metrics. In refining the SGSR

metrics based on lessons learned from the 2009 SGSR, an emphasis was placed on maintaining

consistency for the sake of data continuity.

To solicit stakeholder input regarding ideas for refining the metrics presented in the SGSR, a

series of stakeholder webinars was held by PNNL from May 17th through May 20th, 2010. The

webinars were attended by 54 experts representing electricity service providers, standards

Department of Energy | February 2012

Smart Grid System Report | Page 12

organizations, smart grid demonstration projects, distribution service providers,

telecommunications companies, products and services suppliers, and policy advocacy groups.

The webinars were designed to register feedback regarding metric definition/refinement, data

sources/availability, identification of relevant stakeholder groups, and regional influences. In

reviewing the webinar results, several key messages were identified:

• Most metrics in the 2009 SGSR are well structured and relevant but some are in need of

modification – e.g., metrics regarding cyber security, supervisory control and data

acquisition (SCADA) points, and venture capital funding.

• There are several sources that could be used to close data gaps present in the 2009 SGSR,

including data available through the National Association of Regulatory Utility

Commissioners (NARUC) and state regulators, ARRA projects, expanded data collection

through the EIA, the Smart Grid Maturity Model, and the Database of State Incentives for

Renewable Energy (DSIRE).

• Numerous metrics were identified as relevant but nascent.

• A small number of metrics suffer from poor definition – e.g., metrics regarding microgrids

and regulatory recovery.

• Many additional stakeholders were identified.

• The 2010 SGSR should consider adding a metric that focuses more on environmental and

emissions-reduction goals.

Based on the input received through the webinars, the nascent metrics will remain in the

report due to their potential as significant indicators of long-term growth in a smarter grid but

will be monitored and re-evaluated for future reports. Further, a metric has been added

regarding the percentage of generation through grid-connected renewable resources and the

displaced CO2 emissions attributed to their presence [Metric 21–Grid-Connected Renewable

Resources].

Table 2.1 lists the 21 metrics used in this report. The table includes four columns to indicate

the metric’s status (penetration level/maturity) and trend for both the 2009 and 2010 SGSRs.

The intent is to provide a high-level, simplified perspective to a complicated picture. If it is a

build metric, the penetration level is indicated as nascent (very low and just emerging), low,

moderate, or high; because smart grid activity is relatively new, there are no high penetration

levels to report on these metrics at the present time. If it is a value metric, the maturity of the

system with respect to this metric is indicated as either nascent or mature. Build metrics

describe attributes that are built in support of a smart grid, and value metrics describe the

value that may be derived from achieving a smart grid. The trend (recent past and near-term

projection) is indicated for either type of metric as declining, flat, or growing at nascent, low,

moderate, or high levels. An investigation of the measurements for each metric can be found

in Appendix A of this report.

Department of Energy | February 2012

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Based on the analysis conducted in support of the 2010 SGSR, the following changes have

been made to the status of metrics reported in Table 2.1:

• Metric 2 – The near-term trend for sharing real-time system operations data has been

moved from moderate to high. ARRA investment is expanding the network of PMUs by

877 from the current network of 200 PMUs.

• Metric 3 – The near-term trend for standard distributed-resource interconnection policies

has shifted from moderate to high as 14 states have either implemented new policies or

expanded existing interconnection standards since 2008. As of June 2010, 39 states,

Washington, D.C., and Puerto Rico have adopted variations of an interconnection policy. By

assigning electricity service providers to states based on the location of their headquarters,

it is estimated that roughly 83.9 percent of all electricity service providers in the U.S.

currently have a standard resource interconnection policy in place, compared to 61 percent

in 2008.

• Metric 13 – The current penetration/maturity level for advanced system measurement has

been moved from low to moderate and the near-term trend has been moved to high due to

the aforementioned ARRA-funded PMU projects.

• Metric 18 – Both the penetration/maturity level and the near-term trend associated with

cyber security have been increased from nascent to low because, in 2008, FERC directed

NERC to further tighten the critical infrastructure protection (CIP) standards to provide

external oversight of critical cyber security assets, and removed language allowing variable

implementation of the standards. From 306 CIP violations in July 2008, the number of CIP

violations decreased to 54 in January 2010.

• Metric 19 – The near-term trend was increased from nascent to low because NIST formed

the Smart Grid Interoperability Panel (SGIP) and encouraged smart grid stakeholders from

all organizations associated with electric power to establish this community and advance

interoperability through goals, gap analysis, and prioritized efforts designed to address the

challenges to integration (Widergren et al. 2010). Following a series of stakeholder

workshops, NIST issued Special Publication 1108, the Smart Grid Interoperability Framework

and Roadmap for Smart Grid Interoperability Standards Release 1.0. This document

identified 75 standards that can be applied or adapted to smart grid interoperability or

cyber security needs and identified priority action plans to address 16 standardization gaps

and issues (NIST 2010).

• Metric 21 – The level of renewable resources excluding conventional hydro is approximately

3.5 percent of total generation but is expected to more than quadruple by 2030. Thus, the

current penetration/maturity level assigned to this metric is low while the trend is

moderate.

Department of Energy | February 2012

Smart Grid System Report | Page 14

Table 2.1. Summary of Smart Grid Metrics and Status

2009 SGSR 2010 SGSR

# Metric Title (type: build or value)

Penetration /

Maturity Trend

Penetration /

Maturity Trend

Area, Regional, and National Coordination Regime

1 Dynamic Pricing (build): fraction of customers and total load served by RTP,

CPP, and TOU tariffs.

low moderate low moderate

2 Real-time System Operations Data Sharing (build): total SCADA points

shared and fraction of phasor measurement points shared.

moderate moderate moderate high

3 Distributed-Resource Interconnection Policy (build): percentage of

electricity service providers with standard distributed-resource

interconnection policies and commonality of such policies across electricity

service providers.

moderate moderate moderate high

4 Policy/Regulatory Progress (build): weighted-average percentage of smart

grid investment recovered through rates (respondents’ input weighted

based on total customer share).

low moderate low moderate

Distributed-Energy-Resource Technology

5 Load Participation Based on Grid Conditions (build): Fraction of load served

by interruptible tariffs, direct load control, and consumer load control with

incentives.

low low low low

6 Load Served by Microgrids (build): the percentage of total summer grid

capacity.

nascent low nascent low

7 Grid-Connected Distributed Generation (renewable and non-renewable) and

Storage (build): percentage of distributed generation and storage.

low high low high

8 EVs and PHEVs (build): percentage shares of on-road light-duty vehicles

comprising EVs and PHEVs.

nascent low nascent low

9 Grid-Responsive Non-Generating Demand-Side Equipment (build): total load

served by smart, grid-responsive equipment.

nascent low nascent low

Department of Energy | February 2012

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Table 2.1. (contd)

2009 SGSR 2010 SGSR

# Metric Title (type: build or value)

Penetration /

Maturity Trend

Penetration /

Maturity Trend

Delivery (T&D) Infrastructure

10 T&D System Reliability (value): SAIDI, SAIFI, MAIFI. mature declining mature declining

11 T&D Automation (build): percentage of substations having automation. moderate high moderate high

12 Advanced Meters (build): percentage of total demand served by advanced

metering (AMI) customers.

low high low high

13 Advanced System Measurement (build): percentage of substations

possessing advanced measurement technology.

low moderate moderate high

14 Capacity Factors (value): yearly average and peak-generation capacity

factor.

mature flat mature flat

15 Generation and T&D Efficiencies (value): percentage of energy consumed to

generate electricity that is not lost.

mature improving mature improving

16 Dynamic Line Ratings (build): percentage miles of transmission circuits being

operated under dynamic line ratings.

nascent low nascent low

17 Power Quality (value): percentage of customer complaints related to power

quality issues, excluding outages.

mature declining mature declining

Information Networks, Finance, and Renewable Energy

18 Cyber Security (build): percent of total generation capacity under

companies in compliance with the NERC Critical Infrastructure Protection

standards.

nascent nascent low low

19 Open Architecture / Standards (build): Interoperability Maturity Level – the

weighted average maturity level of interoperability realized between

electricity system stakeholders.

nascent nascent nascent low

20 Venture Capital (value): total annual venture-capital funding of smart grid

startups located in the U.S.

nascent high nascent high

21 Grid-Connected Renewable Resources (build): percent of renewable

electricity, both in terms of generation and capacity.

None None low moderate

RTP = real-time pricing; CPP = critical-peak pricing; TOU = time of use pricing; SAIDI = System Average Interruption Duration Index; SAIFI = System

Average Interruption Frequency Index; MAIFI = Momentary Average Interruption Frequency Index

Department of Energy | February 2012

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2.2 Smart Grid Characteristics

The 21 metrics are used to describe deployment status as organized around the six major

characteristics of a smart grid identified in Table 2.2.

Table 2.2. Smart Grid Characteristics

Characteristic Description

1. Enables

Informed

Participation

by Customers

Consumers become an integral part of the electric power system. They help balance supply

and demand and support reliability by modifying the way they use and purchase electricity.

These modifications come as a result of consumers having choices that motivate different

purchasing patterns and behavior. These choices involve new technologies, new

information about consumers’ electricity use, and new forms of electricity pricing and

incentives.

2. Accommodate

s All

Generation &

Storage

Options

A smart grid accommodates not only large, centralized power plants, but also the growing

array of distributed energy resources (DER). DER integration will increase rapidly all along

the value chain, from suppliers to marketers to customers. Those distributed resources will

be diverse and widespread, including renewables, distributed generation and energy

storage.

3. Enables New

Products,

Services, &

Markets

Markets that are correctly designed and operated efficiently reveal cost-benefit tradeoffs to

consumers by creating an opportunity for competing services to bid. A smart grid accounts

for all of the fundamental dynamics of the value/cost relationship. Some of the

independent grid variables that must be explicitly managed are energy, capacity, location,

time, rate of change, and quality. Markets can play a major role in the management of

these variables. Regulators, owners/operators, and consumers need the flexibility to modify

the rules of business to suit operating and market conditions.

4. Provides

Power Quality

for the Range

of Needs

Not all commercial enterprises, and certainly not all residential customers, need the same

quality of power. A smart grid supplies varying grades of power and supports variable

pricing accordingly. The cost of premium power quality (PQ) features can be included in the

electricity service contract. Advanced control methods monitor essential components,

enabling rapid diagnosis and precise solutions to PQ events, such as arise from lightning,

switching surges, line faults and harmonic sources. A smart grid also helps buffer the

electricity system from irregularities caused by consumer electronic loads.

5. Optimizes

Asset

Utilization &

Operating

Efficiency

A smart grid applies the latest technologies to optimize the use of its assets. For example,

optimized capacity can be attainable with dynamic ratings, which allow assets to be used at

greater loads by continuously sensing and rating their capacities. Maintenance efficiency

involves attaining a reliable state of equipment or “optimized condition.” This state is

attainable with condition-based maintenance, which signals the need for equipment

maintenance at precisely the right time. System-control devices can be adjusted to reduce

losses and eliminate congestion. Operating efficiency increases when selecting the least-

cost energy-delivery system available through these adjustments of system-control devices.

6. Operates

Resiliently to

Disturbances,

Attacks, &

Natural

Disasters

Resilience refers to the ability of a system to react to events such that problematic elements

are isolated while the rest of the system is restored to normal operation. These self-healing

actions result in reduced interruption of service to consumers and help service providers

better manage the delivery infrastructure. A smart grid responds resiliently to attacks,

whether the result of natural disasters or organized by others. These threats include

physical attacks and cyber attacks. A smart grid addresses security from the outset, as a

requirement for all the elements, and ensures an integrated and balanced approach across

the system.

Department of Energy | February 2012

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2.3 Mapping Metrics to Characteristics

Section 3 of the report describes the status of smart grid deployment using the six

characteristics presented in Table 2.2. A map of how the 21 metrics support the six

characteristics is shown in Table 2.3. Notice that nearly every metric contributes to multiple

characteristics. To reduce the repetition of statements about the metrics, each metric was

assigned a primary characteristic for emphasis. The table indicates the characteristic in which a

metric is emphasized as “emphasis.” The other characteristic cells where a metric plays an

important but not primary role are indicated by “mention.” This should not be interpreted to

be of secondary importance, only that a metric finding is mentioned under the characteristic in

order to reduce redundancy of material in explaining the status of smart grid deployment.

Department of Energy | February 2012

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Table 2.3. Map of Metrics to Smart Grid Characteristics

Metric

No. Metric Name

Enables

Informed

Participation

by Customers

Accom-

modates All

Generation

& Storage

Options

Enables New

Products,

Services, &

Markets

Provides

Power Quality

for the Range

of Needs

Optimizes Asset

Utilization &

Efficient Operation

Operates Resiliently to

Disturbances, Attacks,

& Natural Disasters

1 Dynamic Pricing Emphasis Mention Mention Mention

2 Real-Time Data Sharing Mention Emphasis

3 DER Interconnection Mention Emphasis Mention Mention

4 Regulatory Policy Emphasis

5 Load Participation Emphasis Mention Mention Mention

6 Microgrids Mention Mention Emphasis Mention

7 DG & Storage Mention Emphasis Mention Mention Mention Mention

8 Electric Vehicles Mention Mention Emphasis Mention

9 Grid-responsive Load Mention Mention Mention Mention Emphasis

10 T&D Reliability Emphasis

11 T&D Automation Mention Emphasis Mention

12 Advanced Meters Emphasis Mention Mention Mention

13 Advanced Sensors Emphasis

14 Capacity Factors Emphasis

15 Generation, T&D Efficiency Emphasis

16 Dynamic Line Rating Emphasis Mention

17 Power Quality Mention Emphasis

18 Cyber Security Emphasis

19 Open Architecture/Stds. Emphasis

20 Venture Capital Emphasis

21 Renewable Resources Emphasis Mention Mention Mention

Department of Energy | February 2012

Smart Grid System Report | Page 19

3.0 Deployment Trends and Projections

Deploying a smart grid is a journey that has been underway for some time, but will

accelerate because of EISA, ARRA, and the recognition of characteristics and benefits collected

and emphasized under the term “smart grid.” Though there has been much debate over the

exact definition, a smart grid comprises a broad range of technology solutions that optimize the

energy value chain. Depending on where and how specific participants operate within that

chain, they can benefit from deploying certain parts of a smart grid solution set. Based on the

identification of deployment metrics, this section of the report presents recent deployment

trends. In addition, it reviews plans of the stakeholders relevant to smart grid deployments to

provide insight about near-term and future directions.

The status of smart grid deployment expressed in this section is supported by an

investigation of 21 metrics obtained through available research, such as advanced metering and

T&D substation-automation assessment reports, penetration rates for energy resources, and

capability enabled by a smart grid. In each section, the emphasis is placed on data and trends

registered since the 2009 SGSR was completed. In each subsection that follows, the metrics

contributing to explaining the state of the smart grid characteristic are called out so the reader

may review more detailed information in Appendix A. The metrics emphasized to explain the

status of a characteristic are highlighted with an asterisk (*).

3.1 Enables Informed Participation by Customers

A part of the vision of a smart grid is its ability to enable informed participation by

customers, making them an integral part of the electric power system. With bi-directional

flows of energy and coordination through communication mechanisms, a smart grid should

help balance supply and demand and enhance reliability by modifying the manner in which

customers use and purchase electricity. These modifications can be the result of consumer

choices that motivate shifting patterns of behavior and consumption. These choices involve

new technologies, new information regarding electricity use, and new pricing and incentive

programs.

A smart grid adds consumer demand as another manageable resource, joining power

generation, grid capacity, and energy storage. From the standpoint of the consumer, energy

management in a smart grid environment involves making economic choices based on the

variable cost of electricity, the ability to shift load, and the ability to store or sell energy.

Department of Energy | February 2012

Smart Grid System Report | Page 20

Consumers who are presented with a variety of options when it comes to energy purchases

and consumption are enabled to:

• respond to price signals in order to make better-informed decisions regarding when to

purchase electricity, when to generate energy using distributed generation, and whether to

store and reuse it later with distributed storage.

• make informed investment decisions regarding more efficient and smarter appliances,

equipment, and control systems.

Related Metrics

1*, 3, 5*, 7, 8, 9, 12*.

3.1.1 Grid-Enabled Bi-Directional Communication and Energy Flows

A major element of smart grid implementation projects continues to be the deployment of

advanced meters and their supporting infrastructure, or AMI, with ever-increasing numbers of

service providers completing pilot programs and moving toward full AMI deployment [Metric

12–Advanced Meters]. In addition, ARRA allocated $3.4 billion in grants to invest in smart grid

technologies and electricity transmission infrastructure, with total investment of $8.2 billion

including private sector contributions (DOE 2009a).

Smart grid system implementation relies on a variety of AMI technologies that provide two-

way communication between the customer and electricity service provider. Figure 3.1

illustrates the flow of metering data between the consumer Home Area Network (HAN), AMI

technologies, such as smart meters or gateways, and IT systems. HAN communications access

AMI data and can also serve as the gateway from the electricity service provider to the meter.

This communication system can operate through wired, wireless, open or proprietary networks

and supply/communicate information for a variety of consumer and electricity service provider

applications such as energy awareness, demand response, and DG.

Department of Energy | February 2012

Smart Grid System Report | Page 21

Figure 3.1. Overview of AMI Interface (Tendril 2010)

AMI technology can enable the communication of real-time pricing data, grid conditions,

and consumption information. When smart meters are coupled with other enabling

technologies, such as programmable communicating thermostats and data management

systems, information can be gathered and monitored by both the service provider and

consumer. Such data can enable demand response, dynamic pricing and load management

programs.

The number of advanced meters installed in the U.S. has grown dramatically in recent years

from approximately 0.9 million (0.7 percent of all residential meters) in 2006 to 7.95 million in

2009 (FERC 2009b). AMI deployment schedules have accelerated since the passage of ARRA.

Federal grant awards for AMI deployments under ARRA total $812.6 million to date, with total

project values reaching over $2 billion (DOE 2010a). The states with the most significant AMI

investments under ARRA include Texas, Maryland, Maine, and Arizona; however, projects are

being undertaken by electricity service providers located in 19 states (FERC 2009b).

Data on AMI penetration were obtained from the Cleantech Group (Neichin and Cheng

2010) and the EMeter Corporation. Based on data provided by both sources, AMI deployments

nationwide have expanded to an estimated 16 million in 2010, representing 10.7 percent of

U.S. electricity meters. State public utility commissions (PUCs) have approved an additional

34 million AMI deployments. Installed and approved AMI deployments identified by EMeter

(King 2010) are presented in Table 3.1.

Department of Energy | February 2012

Smart Grid System Report | Page 22

Table 3.1. Installed and Planned Smart Meters

Installed AMI Approved AMI

Electricity Service

Provider

# AMI

Units Electricity Service Provider

# AMI

Units

AEP TX 0.1 M AEP TX 0.9 M

Alliant 0.5 M Alliant 0.9 M

CenterPoint 0.5 M CenterPoint 1.9 M

Delmarva 0.2 M Delmarva 0.2 M

Exelon 0.2 M Exelon 2.0 M

FPL 0.6 M FPL 3.9 M

Idaho Power 0.1 M Idaho Power 0.4 M

Oncor 1.3 M Oncor 1.7 M

PG&E 6.5 M PG&E 3.6 M

SDG&E 1.4 M SDG&E 1.1 M

Southern Company 1.0 M Southern Company 3.6 M

PPL 1.4 M Baltimore Gas and Electric

Company 2.0 M

SCE 1.4 M Bluebonnet 0.1 M

PGE 0.8 M Burbank Water & Power 0.1 M

AEP OH 0.2 M CPS Energy 1.0 M

Pepco 0.8 M

SCE 3.6 M

SCG 6.0 M

Silicon Valley Power 0.1 M

TNMP 0.2 M

Westar Energy 0.1 M

Total 16.5 M Total 34.2 M

Demand response technologies, which involve bi-directional flows of information between

home equipment and the grid, hold promise for reducing peak demand. An evaluation

conducted by FERC found that demand response technologies hold the potential to reduce

peak demand by 14 to 20 percent by 2019 under achievable and full-participation scenarios,

respectively (FERC 2009a). A significant portion of peak-demand reductions rely on electricity

service providers offering dynamic pricing tariffs, which require enabling technologies such as

smart meters and communicating thermostats [Metric 1–Dynamic Pricing]. Generally, these

tariffs take the following forms:

• Time of use (TOU). Under TOU, prices are differentiated based solely on a peak versus off-

peak period designation, with prices set higher during peak periods. TOU pricing is not

dynamic because it does not vary based on real-time conditions. It is included here, though,

because it is viewed as an intermediate step toward a more dynamic real-time pricing (RTP)

tariff.

• Critical peak pricing (CPP). Under a CPP tariff, the higher critical-peak price is restricted to a

small number of hours (e.g., 100 of 8,760) each year, with the peak price being set at a

much higher level relative to normal conditions.

Department of Energy | February 2012

Smart Grid System Report | Page 23

• Real-time pricing. Under RTP, hourly prices vary based on the day-of (real time) or day-

ahead cost of power to the electricity service provider.

FERC conducts biennial interviews regarding demand response initiatives, pricing tariffs, and

AMI deployments. In 2008, the FERC questionnaire was distributed to 3,407 organizations in all

50 states. In total, 100 electricity service providers that responded reported offering some

form of RTP tariff to enrolled customers, as compared to 60 in 2006 (Table 3.2). FERC also

found through these interviews that 315 electric service providers nationwide offered TOU

rates, compared to 366 in 2006. In 2008, 241 of the 315 electricity service providers with TOU

rates reported offering those rates to residential customers. In those participating electricity

service providers, approximately 1.3 million customers were signed up for TOU tariffs,

representing 1.1 percent of all residential homes. In 2008, customers were enrolled in CPP

tariffs offered by 88 electricity service providers, as compared to 36 in 2006. The programs

reported in Table 3.2 include those offered to residential, commercial, and industrial

customers.

Table 3.2. Number of Entities Offering and Customers Served by Dynamic Pricing Tariffs

(FERC 2008)

Method of Pricing

Number of

Entities in

2006

Number of

Entities in

2008

Customers Served

Number

Share of

Total

Real-Time Pricing 60 100 -- --

Critical-Peak Pricing 36 88 -- --

Time-of-Use Pricing 366 315 1,270,000 1.1%

Electricity service providers interviewed for this report were asked two questions related to

dynamic pricing. The first question asked respondents: Do you have dynamic or supply-based

price plans?

• Twelve companies (50 percent) indicated no dynamic price plans were in place.

• Twelve companies (50 percent) indicated they offered TOU plans.

• No companies offered CPP plans.

• One company (4.2 percent) indicated they had both dynamic price plans and the ability to

send price signals to customers.

The respondents were also asked whether their electricity service provider had automated

responses to pricing signals for major energy using devices within the premises. Responses

were as follows:

• Fifteen companies (62.5 percent) indicated there were none.

Department of Energy | February 2012

Smart Grid System Report | Page 24

• Seven companies (29.2 percent) indicated that automated price signals for major energy

using devices were in the development stage.

• Two companies (8.3 percent) indicated that a small degree of implementation (10 to

30 percent of the customer base) had occurred.

The results of recent voluntary programs suggest that the impact of dynamic pricing could

be significant. In 2008, the Pacific Gas and Electric Company (PG&E) began their residential

SmartRate program, which offered voluntary CPP tariffs to approximately 10,000 customers. By

the end of 2009, over 25,000 customers had signed up for the program (George et al. 2010).

The program raised rates incrementally during the afternoon peak period (2 p.m. to 7 p.m.) up

to as high as $.60 per kWh for residential customers and $.75 per kWh for non-residential

customers (George et al. 2010). The results of the program indicate that the incrementally

higher rates resulted in reductions in peak-period energy use by an average of 15 percent by

residential customers and 7.5 percent by low-income residential customers; average load

reductions increased to 19.2 percent when customers were successfully notified of the event

(George et al. 2010). Participants were offered bill protection, credits and financial incentives

(gift cards) for enrollment.

In the future, as EVs and PHEVs penetrate the U.S. light-duty vehicle market, these

alternative-fuel vehicles could also advance load shifting through their energy storage

capabilities [Metric 8–EVs and PHEVs]. Vehicle-to-grid (V2G) software could be used to perform

several functions while vehicles are connected to the grid: (i) adjust the timing and pace of

charging to meet the needs of the customer while minimizing the demand placed on the grid;

(ii) upload real-time performance data and vehicle information such as the car battery’s size,

current state of charge, elapsed time since the last charge, and vehicle miles traveled (VMT);

and (iii) enable EVs to charge during periods of low demand and return stored energy back to

the grid during peak periods. Several pilot tests are being conducted across the U.S. to examine

various charging management strategies. These tests include:

• Idaho National Laboratory (INL) is leading a field test of 57 PHEVs with the objective of

capturing real-time data from vehicles in Washington, Oregon, California, and Hawaii.

• Seattle City Light is operating a field test on 13 Toyota Priuses to examine the impact of a

PHEV fleet deployed in an urban environment.

• Duke Energy, Progress Energy, and Advanced Energy are leading a field test involving the

smart charging of 12 Toyota Priuses to examine the requirements for supporting vehicles as

they roam between service areas (V2 Green 2010).

Charging controls will be necessary to minimize the impact of EVs and PHEVs on electricity

service providers. Off-peak (nighttime) charging will minimize the need for equipment

upgrades on the electrical distribution system. Recent research on the impacts of Level 1

Department of Energy | February 2012

Smart Grid System Report | Page 25

(120V) and Level 2 (240V) charging on the electricity delivery system points to the potential for

overloading distribution transformers, fuses, switches, and regulators on distribution feeders

depending on the density of early adopters of EVs and PHEVs, particularly when a high

concentration of Level 2 charging is expected (Gerkensmeyer et al. 2010, Onar and Khaligh

2010). In response to this concern, electricity service providers in California (e.g., City of Palo

Alto Utilities and Burbank Water and Power) are working to identify where EVs and PHEVs are

likely to first appear in order to plan for the increased demand in a manner that will reduce the

possibility of an early setback in the effort to enhance EV and PHEV penetration and reduce

petroleum consumption.

In 2008, EIA reported that 9,591 service provider- or customer-owned distributed

generators were grid-connected, representing a total capacity of 12,863 megawatts (MW) (EIA

2008). In addition, EIA reported 12,262 dispersed generators (not grid-connected),

representing 9,773 MW. When compared to 2006 levels, the number of distributed and

dispersed generators has grown by 90.1 percent and 28.6 percent, respectively [Metric 3–

Distributed-Resource Interconnection Policy].

DG has the capacity to help alleviate peak load, provide needed system support during

emergencies, and improve power quality and reliability [Metric 7–Grid-Connected Distributed

Generation]. Service providers that facilitate the integration of these resources and use them

effectively could realize considerable cost savings over the long-term.

Consumer participation in DG can be facilitated with agreed-upon policies for

interconnection to the grid. As of June 2010, 39 states, Washington D.C., and Puerto Rico have

adopted variations of interconnection policies. Since 2008, 14 states have either implemented

new policies or expanded interconnection standards, representing 83.9 percent of electricity

service providers in the U.S. This is an increase of 22.9 percent since the previous SGSR was

released in 2009.

The presence of an interconnection policy, however, does not necessarily indicate that the

policy is favorable to electricity consumers or even equitable to both parties. In 2009, the

Interstate Renewable Energy Council (IREC) and the Network for New Energy Choices (NNEC)

analyzed the favorability of state interconnection standards based on a 14-point numerical

grading system that awarded points for active promotion and deducted points for discouraging

advancement of DER. The grading system designed by IREC and NNEC numerically evaluated

14 policy issues specific to interconnection, including: technological considerations, system

capacity, cost effectiveness, insurance requirements, and timelines (NNEC 2009). Based on

interconnection standards measured by IREC and NNEC, 13 states have policies favorable to

grid interconnection, 15 states have neutral policies and 22 states (including those with no

standard) have unfavorable policies [Metric 3–Distributed-Resource Interconnection Policy].

Department of Energy | February 2012

Smart Grid System Report | Page 26

3.1.2 Managing Supply and Demand

Simple measures, such as turning off or adjusting water heaters, dishwashers, and heating

and cooling systems, result in load shifting and reduced costs through the smoothing of peak

power consumption throughout the day. With appropriate metering capability in place,

dynamic pricing signals received by customers can enable demand response.

Traditionally, demand participation has principally taken place through interruptible

demand and direct-control load-management programs implemented and controlled by

electricity suppliers. While many organizations (e.g., Electric Reliability Council of Texas

(ERCOT), Public Utility Commission of Texas (PUCT), and ISOs in California and New York) act to

balance and curtail load in order to avoid and manage brownouts and blackouts, load

management participation is very low nationally, as indicated in Figure 3.2.

Figure 3.2 demonstrates that load management has not historically played a strong role in

energy markets. Nationally, load management as a percentage of net summer capacity was

1.3 percent in 2008. The trend has been somewhat volatile over the past decade but has

appeared to follow an upward trend since 2003. According to the EIA, load management in

2008 reached 13,091 MW (EIA 2010f). Thus, less than 2 percent of net summer capacity is

under load management programs [Metric 5–Load Participation Based on Grid Conditions].

Figure 3.2. National Historic Demand-Response and Load-Management Peak Reduction as a

Percentage of Summer Net Capacity

Despite the load management shares presented in Figure 3.2, FERC forecasts growth in

demand-response programs under its business-as-usual (BAU) case, with peak demand

reductions reaching 38 GW, or 4 percent, by 2019. Demand can also be managed through

Department of Energy | February 2012

Smart Grid System Report | Page 27

engaging appliances, thermostats, and other equipment that hold the potential to be

responsive to the dynamic needs of the electricity system. Products have emerged and

continue to evolve in this category that either directly monitor or receive communicated

recommendations from system operators. A recent report prepared for the California Energy

Commission notes that 69 percent of California residents have programmable thermostats,

with 36 percent of those capable of two-way communication (Palmgren et al. 2010). Based on

EIA electricity customer data, the forecast penetration rate corresponds to approximately 3.7

million electricity customers in California with communicating thermostats in 2009. Progress is

being made with “smart” appliances as well. Zpryme Research and Consulting projects that the

U.S. smart appliance market will expand from $1.42 billion in 2011 to $5.46 billion in 2015,

representing a nearly 40 percent growth rate. Clothes washers and dryers are expected to

make up 36 percent of the market while refrigerators and freezers are forecast to comprise 24

percent of the market. Further, Whirlpool expects to make all appliances smart grid capable by

2015 (Zpryme Research and Consulting 2010). [Metric 9–Grid-Responsive Non-Generating

Demand-Side Equipment]. Although markets for these products are still nascent, deployment

of smart grid technologies, infrastructure and policies will enhance penetration of demand-

response devices.

Though dynamic-pricing and demand-response programs have historically been responsible

for modest levels of load shifting, current research suggests that there is significant potential

for the programs to manage supply and demand in the future. A recent study sponsored by

EPRI and the Edison Electric Institute (EEI) estimated that 37 percent of the growth in electricity

sales (419 TWh) between 2008 and 2030 could be offset through energy-efficiency programs

and 52 percent of peak demand growth (164 GW of capacity) could be offset by a combination

of energy-efficiency and demand-response programs. More specifically, approximately

2,824 MW of peak demand was forecasted to be offset by 2010 through price-responsive

policies, 13,661 MW of peak demand could be offset through price response by 2020, and

24,869 MW could be offset by 2030. The largest share of the price-response benefits are

forecast to take place in the residential sector (10,838 MW or 43.6 percent of the offset in

2030), with the commercial sector (8,350 MW or 33.6 percent of the offset in 2030) and

industrial sector (5,681 MW or 22.8 percent of the offset) trailing behind (Rohmund et al.

2008). Figure 3.3 illustrates the potential savings from demand-response and energy-efficiency

programs by sector, as estimated by EPRI and EEI.

Figure 3.3. 2030 Forecast Dema

Sector

3.2 Accommodating All Generation and Storage Options

Central to the concept of a smart grid is the ability to accommodate a range of diverse

generation types including centralized and distributed

options. Using different generation and storage types, a smart grid can better meet consumer

load demand, as well as accommodate intermittent renewable energy technologies.

Specifically, distributed resources can he

during emergencies, and reduce the costs of power. Accommodating the wide range of options

available to transmission providers, distribution entities, and end users requires an

environment similar to the computer industry’s “plug and play” environment (DOE

The primary metrics of progress for this characteristic include the amount of grid

DG and storage, progress in connecting diverse generation types, a standard distributed

resource connection policy, and grid

other metrics (e.g., microgrids, electric vehicles, AMI) that also describe the current status of a

smart grid to accommodate all generation and storage options, and these me

addressed in this section of the report.

Related Metrics

1, 3*, 6, 7*, 8, 9, 12, 21*.

Measures of DG [Metric 7–Grid

standards policies [Metric 3–Distributed Resource Interconnecti

positive directions. DG systems are smaller

can be connected to primary and/or secondary distribution voltages as compared to the larger,

Department of Energy

Smart Grid System

2030 Forecast Demand Response and Energy Efficiency Peak Demand Offsets by

Accommodating All Generation and Storage Options

Central to the concept of a smart grid is the ability to accommodate a range of diverse

generation types including centralized and distributed generation, as well as diverse storage

options. Using different generation and storage types, a smart grid can better meet consumer

load demand, as well as accommodate intermittent renewable energy technologies.

Specifically, distributed resources can help meet peak demand, supply needed system support

during emergencies, and reduce the costs of power. Accommodating the wide range of options

available to transmission providers, distribution entities, and end users requires an

omputer industry’s “plug and play” environment (DOE

The primary metrics of progress for this characteristic include the amount of grid

DG and storage, progress in connecting diverse generation types, a standard distributed

connection policy, and grid-connected renewable resources. There are a number of

microgrids, electric vehicles, AMI) that also describe the current status of a

smart grid to accommodate all generation and storage options, and these metrics are also

addressed in this section of the report.

Grid-Connected Distributed Generation] and the interconnection

Distributed Resource Interconnection Policy] are moving in

positive directions. DG systems are smaller-scale, local power generation (10 MVA or less) that

can be connected to primary and/or secondary distribution voltages as compared to the larger,

Department of Energy | February 2012

Smart Grid System Report | Page 28

nd Response and Energy Efficiency Peak Demand Offsets by

Accommodating All Generation and Storage Options

Central to the concept of a smart grid is the ability to accommodate a range of diverse

generation, as well as diverse storage

options. Using different generation and storage types, a smart grid can better meet consumer

load demand, as well as accommodate intermittent renewable energy technologies.

lp meet peak demand, supply needed system support

during emergencies, and reduce the costs of power. Accommodating the wide range of options

available to transmission providers, distribution entities, and end users requires an

omputer industry’s “plug and play” environment (DOE 2008).

The primary metrics of progress for this characteristic include the amount of grid-connected

DG and storage, progress in connecting diverse generation types, a standard distributed-

connected renewable resources. There are a number of

microgrids, electric vehicles, AMI) that also describe the current status of a

trics are also

Connected Distributed Generation] and the interconnection

on Policy] are moving in

scale, local power generation (10 MVA or less) that

can be connected to primary and/or secondary distribution voltages as compared to the larger,

Department of Energy | February 2012

Smart Grid System Report | Page 29

more centralized generation that provides most of the grid’s power (IEEE 2003). Incentives to

promote installation of such systems are becoming more common at the state level. In

November and December 2010 alone, 20 states instituted new incentive policies or expanded

existing ones (DSIRE 2010a). Solar cells, solar thermal electricity systems, wind turbines and

biomass applications are some of the options available to residential and rural consumers.

Batteries, flywheels and thermal storage units that can be used to store energy are also

included in this category.

Other measures that affect this category include dynamic pricing [Metric 1], microgrids

[Metric 6–Load Served by Microgrids], market penetration of EVs and PHEVs [Metric 8], grid-

responsive, non-generating demand-side equipment [Metric 9], advanced meters [Metric 12]

and grid-connected renewable resources [Metric 21]. Each measure plays a unique role in

accommodating all generation and storage. The microgrid metric still remains nascent and

largely unmeasured. EVs, another source of distributed resources, reached almost 27 thousand

vehicles on the road in 2008 or almost 0.01 percent of light-duty vehicles. Demand-side

equipment is a nascent metric and is still unmeasured at this time. Grid-connected renewable

resources [Metric 21] include more than DG, as wind farms and other large but decentralized

sources of generation are also encompassed within this category. Currently, renewable

resources excluding conventional hydro have reached more than 3.5 percent of total

generation and total output is expected to more than quadruple by 2030.

A wide range of stakeholders including end users, service providers, regulators,

manufacturers, distribution service providers and third party developers must be

accommodated in order to attain all the generation and storage options available. Appropriate

consideration will need to be given to each stakeholder’s interest to support the appropriate

evolution of the electricity system while improving efficiency of grid resources (DOE 2008). For

example, stakeholders associated with development of generation and storage systems and

other technologies will need to recoup their investments and must have clear incentives in

order to initiate projects. Storage device life-cycle benefits must be greater than costs and

pricing approaches need to be structured to provide the benefits to owners. Similarly, while

distributed generation offers benefits in terms of electricity generation plant investment cost

avoidance to power producers, end users need to see the additional benefits, including greater

reliability and less environmental impact, when compared with traditional fossil fuel systems.

In some cases, utilities or regulators even impose fees upon the owner when DG often saves

the utility money in deferred upgrades, reduced losses, and avoidance of wholesale purchases

to cover demand (Gil and Joos 2008). Both end users and service providers must recover their

investments in distributed resources, smart meters, and other smart grid accessories that allow

the grid resources and entities to communicate and respond to changing grid conditions

(NETL 2007). The following sections describe in more detail distributed generation and storage,

and interconnection standards.

Department of Energy | February 2012

Smart Grid System Report | Page 30

3.2.1 Distributed Generation and Storage

Distributed generation capacity [Metric 7–Grid-Connected Distributed Generation]

continues to be a small part of total power generation even though it has been steadily

increasing over the years. Total DG capacity reached 5,423 MW in 2004, then grew to

12,863 MW in 2008, an increase of 137 percent (Figure 3.4) (EIA 2010a). With the recession in

2008, electricity generation and sales were adversely affected by the weakening economy as

net electric power generation decreased for the first time since 2001, dropping 0.9 percent

from 4,157 million megawatt-hours (MWh) in 2007 to 4,119 million MWh in 2008. Non-

coincident summer peak load fell by 3.8 percent, from 782,227 MW in 2007 to 752,470 MW in

2008. Non-coincident winter peak load increased in 2008 by 0.9 percent, from 637,905 MW in

2007 to 643,557 MW in 2008 (EIA 2010a).

Figure 3.4. Yearly Installed DG Capacity by Technology Type (EIA 2010c)

Actively managed fossil-fired, small hydro, and biofuels DG capacity reached 10,121 MW in

2008, up 136 percent from 2004, representing approximately 1.3 percent of total generating

capacity and 78 percent of total DG (Table 3.3). Wind and other renewable energy sources

(RESs) grew significantly between 2004 and 2008, increasing by 1,051 percent. That level

represents only 0.16 percent of total available generating capacity, 0.21 percent of summer

peak capacity, and 0.24 percent of winter peak (EIA 2010c). Distributed wind is very small in

comparison to central wind farms, which had nearly 23,000 MW of capacity. Intermittent

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

2004 2005 2006 2007 2008

Internal Combustion Combustion Turbine Steam Turbine Hydro-electric Wind and Other Total

Department of Energy | February 2012

Smart Grid System Report | Page 31

renewable-energy resources such as wind may not be effective sources for meeting peak

demand, although solar has the potential to be more coincident with summer peak-demand

periods.

Interviews conducted in support of this study indicated the following about grid-connected

DG:

• The capacity to support DG (connect and use DG) is 23.7 percent of total grid capacity.

• Storage capacity comprised of approximately 0.9 percent of total customers.

• Non-dispatchable renewable generation was reported by 5.7 percent of total customers,

compared to 1.4 percent measured for the 2009 SGSR.

Table 3.3. Yearly Installed DG Capacity by Technology Type (EIA 2010c)

Capacity of Distributed Generators by Technology Type , 2004 through 2008

(Count, Megawatts)

Period

Internal

Combustion

Combustion

Turbine

Steam

Turbine Hydroelectric

Wind and

Other Total

Capacity Capacity Capacity Capacity Capacity

Number

of Units Capacity

2004 2,169 1,028 1,086 1,003 137 5,863 5,423

2005* 4,024 1,917 1,831 998 994 17,371 9,766

2006 3,625 1,299 2,580 806 1,078 5,044 9,641

2007 4,614 1,964 3,595 1,053 1,427 7,103 12,702

2008 5,112 1,949 3,060 1,154 1,588 9,591 12,863

* Distributed generator data for 2005 includes a significant number of generators reported by one respondent that may be for

residential applications.

Note: Distributed generators are commercial and industrial generators that are connected to the grid. They may be installed at

or near a customer’s site, or elsewhere. They may be owned by either the customers of the distribution service provider or by

the electricity service provider. Other Technology includes generators for which technology is not specified.

Some DG systems have large startup costs for customers. For example, solar panels can be

easily installed on rooftops by homeowners and safely generate power for years. However,

solar power installed in this way can have a cost of $6 per watt (NREL 2010), although in the

future these costs could become much lower even including installation (Next Energy News

2007). Cost reductions are expected as DG capacities grow. More specifically, the costs for DG

technologies are expected to fall by 10 percent for each of the first three doublings of capacity,

5 percent during the next five doublings of capacity, and finally by 2.5 percent for all

subsequent capacity doublings (Eynon 2002).

3.2.2 Standard Distributed-Resource Connection Policy

The increasing presence of DER has led to various efforts to standardize the process of

interconnecting these resources with the grid. Federal legislation attempting to deal with the

Department of Energy | February 2012

Smart Grid System Report | Page 32

issue emerged in progressively stronger language, resulting in the Energy Policy Act of 2005

(EPACT 2005), which requires all state and non-state electricity service providers to consider

adopting interconnection standards based on the Institute of Electrical and Electronics

Engineers (IEEE) Standard 1547 (42 USC 15801). IEEE 1547, published in 2003, looks strictly at

the technical aspects of DER interconnection, providing a standard that limits the negative

effects of these resources on the grid (Cook and Haynes 2006). Currently, IEEE is working on

Standards 1547.6 and 1547.8, which will expand the functional requirements for DER

interconnection.

To expand favorability of interconnection standards, EISA mandated interoperability policies

to accommodate consumer distributed resources, including DG, renewable generation, energy

storage, energy efficiency and demand response (110 USC 1305). By June 2010, 39 states as

well as Washington, D.C. and Puerto Rico adopted variations of an interconnection policy

[Metric 3–Distributed Resource Interconnection Policy]. Distributed resource interconnection

policies have been either implemented or expanded in 14 states since 2008, thus promoting the

advancement of distributed generation technologies. The percentage of electricity service

providers with standard resource-interconnection policies was based on whether the individual

electricity service providers matched their state’s interconnection policies. Roughly 83.9

percent of electricity service providers currently have a standard resource-interconnection

policy in place, compared with 61 percent in 2008 (NNEC 2009).

States differ significantly in their approach to interconnection standards. Nine states plus

Puerto Rico have no limits on the size of generation systems allowed within their programs;

18 states limit generator interconnection based on energy type or kilowatt (kW) capacity; and

13 states limit their standards to net-metering systems only (Figure 3.5). Many states that have

taken aggressive action on DG have done so to incorporate grid-connected renewable energy to

meet renewable portfolio standards or energy efficiency requirements.

Department of Energy | February 2012

Smart Grid System Report | Page 33

Figure 3.5. State Interconnection Standards (DSIRE 2010b)

In order for interconnection standards to be acceptable to end users, states should draft

them in a manner that encourages consumer participation. The IREC and the NNEC analyzed

the favorability of state interconnection standards in 2009 based on a 14-point numerical

grading system that awarded points for active promotion and deducted points for discouraging

DER advancement (NNEC 2009). Previously, the 2009 SGSR used research conducted as part of

EPA’s clean energy programs. EPA conducted a similar study in 2008, which was used to

evaluate the favorability of interconnection standards for distributed generation. The EPA

based their favorability standards on six factors that affect interconnection policy. Table 3.4

illustrates the difference between the IREC/NNEC and EPA favorability scoring categories.

Table 3.4. Favorability Scoring Categories

IREC & NNEC Policy Grading Categories Factors Affecting DG-Friendliness of

Interconnection Standards

Eligible Technologies Standard Interconnection Forms

Individual System Capacity Simplified Procedure for Small Systems (≤ 10 MW)

“Breakpoints” for Interconnection Process Timelines

Timelines System Size Limits

Interconnection Charges Insurance Requirements

Engineering Charges Technical Requirements

External Disconnect Switch

Certification

Technical Screens

Network Interconnection

Standard Form Agreement

Insurance Requirements

Dispute Resolution

Department of Energy | February 2012

Smart Grid System Report | Page 34

Rule Coverage

Figure 3.6 presents the favorability of interconnection standards in each state according to

the IREC and NNEC study. The A-F grading system used in the IREC and NNEC study was

established on the basis of the categories listed in Table 3.4 to reflect positive or negative

implementation characteristics for each component. The IREC and NNEC study found that

13 states have favorable policies, 15 states have neutral policies and 22 states (including those

with no standard) have unfavorable policies for grid interconnection. Results from the

2009 study are similar to those of the EPA study which determined that out of all states with

interconnection standards enacted, 15 states had favorable policies, 12 states had neutral

policies and 5 states had unfavorable policies (NNEC 2009). The IREC and NNEC study indicated

significantly more states had unfavorable standards.

Figure 3.6. Favorability of State Interconnection Standards According to IREC and NNEC

3.3 Enables New Products, Services, and Markets

Energy markets that are correctly designed and operated can efficiently reveal benefit-cost

tradeoffs to consumers by creating an opportunity for competing services to bid. A smart grid

enables a more dynamic monitoring of the value/cost relationship by acquiring real-time

information, conveying information to consumers, and supporting variable pricing policies that

promote consumer responses to price signals. Some of the grid variables that must be explicitly

managed are energy, capacity, location, time, rate of change, and quality. Markets can play a

major role in the management of those variables. Regulators, owner/operators, and

consumers need the flexibility to modify the rules of business to suit operating and market

conditions.

Related Metrics

Department of Energy | February 2012

Smart Grid System Report | Page 35

1, 3, 4*, 7, 8*, 9, 12, 17,

19*, 20*, 21.

Smart grid investments are often capital intensive and include multiple jurisdictions within

an electricity provider’s service area. Thus, while smart grid investments can achieve numerous

operational efficiencies (e.g., reduce meter-reading costs, require fewer field visits, enhance

billing accuracy, improve cash flow, improve information regarding outages) and enable

numerous new products (e.g., PHEVs, smart appliances, solar panels), such benefits may be

difficult to quantify and build into existing business cases.

To address current regulatory and financial barriers to smart grid implementation, Congress

has responded with legislation designed to encourage development of new, advanced

technologies in the energy sector. In 2009, ARRA designated $4.5 billion in funding for electric

grid modernization programs, including $3.4 billion for the Smart Grid Investment Grant (SGIG)

Program. To date, ARRA has resulted in grants being awarded to 99 recipients, including

private companies, service providers, manufacturers and cities, with total public-private

investment amounting to over $8 billion. Figure 3.7 maps projects that are currently underway,

including both ARRA and non-ARRA smart grid demonstration projects. These projects, along

with other recent initiatives at the state level and within private industry, are lighting a path

towards the development of new and innovative smart grid-enabled products, services, and

markets.

Figure 3.7. ARRA and Non-ARRA Smart Grid Investment

(SGIC 2010)

3.3.1 Enabling New Products and Services

A smart grid enables new products and services through automation, communication

sharing, facilitating and rewarding shifts in customer

market conditions, and its ability to encourage development of new technologies (e.g.,

PHEVs). A smart grid enables grid

thermostats, microwaves, clothes washers

Responsive Non-Generating Demand

and General Electric (GE) began a joint smart

families of Reliant employees in Texas. The project’s goal is to demonstrate how a typical

family could employ smart-grid-connected washing machines, dryers, and refrigerators to

manage their home energy use (Ordans News 2010). GE (2009) has also been working with

Louisville Gas and Electric (LG&E) over the past year on a joint smart grid demonstration project

in Louisville, Kentucky, to test the interaction between smart appliances and smart meters

under dynamic pricing conditions.

Department of Energy

Smart Grid System

ARRA Smart Grid Investment Grant and Demonstration Projects

Enabling New Products and Services

A smart grid enables new products and services through automation, communication

sharing, facilitating and rewarding shifts in customer behavior in response to changing grid and

market conditions, and its ability to encourage development of new technologies (e.g.,

PHEVs). A smart grid enables grid-responsive equipment, including communicating

thermostats, microwaves, clothes washers and dryers, and water heaters [Metric 9

Generating Demand-Side Equipment]. On November 4th, 2009, Reliant Energy

and General Electric (GE) began a joint smart-appliance demonstration project in the homes of

s in Texas. The project’s goal is to demonstrate how a typical

connected washing machines, dryers, and refrigerators to

manage their home energy use (Ordans News 2010). GE (2009) has also been working with

d Electric (LG&E) over the past year on a joint smart grid demonstration project

in Louisville, Kentucky, to test the interaction between smart appliances and smart meters

under dynamic pricing conditions.

Department of Energy | February 2012

Smart Grid System Report | Page 36

and Demonstration Projects

A smart grid enables new products and services through automation, communication

behavior in response to changing grid and

market conditions, and its ability to encourage development of new technologies (e.g., AMI,

responsive equipment, including communicating

and dryers, and water heaters [Metric 9–Grid-

, 2009, Reliant Energy

appliance demonstration project in the homes of

s in Texas. The project’s goal is to demonstrate how a typical

connected washing machines, dryers, and refrigerators to

manage their home energy use (Ordans News 2010). GE (2009) has also been working with

d Electric (LG&E) over the past year on a joint smart grid demonstration project

in Louisville, Kentucky, to test the interaction between smart appliances and smart meters

Department of Energy | February 2012

Smart Grid System Report | Page 37

A smart grid that incorporates real-time pricing structures and bi-directional information

flow through metering and information networks is expected to support the introduction of

numerous technologies into the system. Enabling AMI technology itself represents a major

driver in smart grid investment, as evidenced by several large-scale deployment programs

[Metric 12–Advanced Meters]:

• PG&E, which operates in California, has invested $466 million to install 5.8 million gas and

electric meters by June 2010; full deployment is projected by 2012 (CPUC 2009).

• DTE Energy (2009), operating in Michigan, invested $84 million to install 0.7 million smart

meters in their service area in 2010.

• American Electric Power (AEP), which has a large service area in the Midwest and South,

plans to install up to 5 million meters, with regulatory approval, through their gridSmart

program by 2015. Regulatory support has been approved for deployment of 1.25 million

meters in Texas, Ohio and Oklahoma and the deployment will be completed in 2014. Total

investment will top $375 million for the Texas, Ohio and Oklahoma regions.

• Southern California Edison (SCE) plans to install 5 million meters by 2012 (SCE, undated).

• Connecticut Light & Power (CL&P) (2010) will offer dynamic pricing programs through AMI

to 1.2 million customers beginning in 2012.

A smart grid also supports the deployment of new vehicle technologies (EVs and PHEVs)

[Metric 8–EVs and PHEVs]. The various features of a smart grid, including bidirectional

metering and dynamic pricing, could feasibly enhance the customer’s return on investment

(ROI) for EV and PHEV technologies and accelerate market penetration. Furthermore, smart

grid elements support financial incentives that could lead to a shift in charging off-peak, which

could reduce the need for additional investments in energy infrastructure and enhance

infrastructure asset utilization rates as more EVs and PHEVs are put into operation. Thus, the

market penetration of EVs and PHEVs demonstrates the potential application of new

technologies enabled by smart grid capabilities.

Table 3.5 shows that the number of EVs reached 26,823 in 2008, representing roughly

.01 percent of all light-duty vehicles in use. Light-duty vehicles include automobiles, vans,

pickups, and sport utility vehicles (SUVs) with a gross vehicle weight rating of 8,500 pounds or

less.2 Annual PHEV sales are forecast by DOE to reach 142,358 (0.9 percent of light-duty vehicle

sales) by 2020 and 408,498 (2.3 percent of light-duty vehicle sales) by 2030. PHEV’s in use are

forecast by DOE to reach 3.3 million (1.2 percent of all light-duty vehicles) by 2030 (EIA 2010d).

2 The definition of light-duty vehicles includes motorcycles. Although electric motorcycles are commercially

available, plug-in hybrid motorcycles are unlikely to be pursued as a product. Therefore, we omitted motorcycles

from this analysis.

Department of Energy | February 2012

Smart Grid System Report | Page 38

Table 3.5. EV and PHEV Market Penetration (EIA 2010d)

Customer acceptance of EVs and PHEVs will soon be put to the test with the newly

introduced Nissan Leaf, which has a 100-mile all-electric range, the Tesla Roadster, and the

2011 Chevrolet Volt, which is a PHEV with an all-electric range of 40 miles. In addition to the

Volt, there are several companies that perform aftermarket PHEV conversions, including

Amberjac Systems, Hybrids-Plus, Plug-In Conversions Corp., and Hymotion. Additionally, ARRA

funded $2.4 billion in grant awards for electric vehicle battery development, component

manufacturing and transportation electrification (DOE 2009c). Grants were awarded to

48 projects conducted by private manufacturing companies, universities and automotive

corporations.

The U.S. DOE forecast presented in the 2010 Annual Energy Outlook (AEO) is very

conservative compared to a number of recent forecasts prepared by industry. While some

forecasts estimate ultimate EV and PHEV penetration in the 8 to 16 percent range, more recent

forecasts that use more aggressive assumptions regarding public investment in R&D,

advancements in battery technology, oil prices, and tax incentives for consumers have

estimated PHEV market penetration rates as high as 60 to70 percent of the light duty vehicle

market by 2030. For example, the EPRI and Natural Resources Defense Council (NRDC)

estimated PHEV market penetration rates under three scenarios, ranging from 20 to 80 percent

(medium PHEV scenario estimate of 62 percent) in 2050. EPRI and NRDC used a consumer-

choice model to estimate market penetration rates (EPRI et al. 2007).

The findings of the EPRI and NRDC study, as well as those for several other EV and PHEV

market penetration studies, are presented in Figure 3.8. Note that for several studies, there are

multiple estimates representing forecast penetration rates at various future points in time.

Further, some of the studies presented a range of estimates for single points in time based on

various policy or technology assumptions. These studies are designated through high-low

points connected with lines in the graph. Each of the studies identified in Figure 3.8 is

examined in more detail under Metric 8 in Appendix A of this report.

EVs On-Road PHEVs On-Road EV Sales PHEV Sales

Year Total in Use% of Light-Duty

Vehicles Total in Use% of Light-Duty

Vehicles Total Sales% of Light-Duty

Market Total Sales% of Light-Duty

Vehicles

2008 26,823 0.01% - 0.00% 120 0.00% - 0.00%

2010 24,168 0.01% - 0.00% 96 0.00% - 0.00%

2015 17,738 0.01% 243,859 0.10% 146 0.00% 89,173 0.54%

2020 11,360 0.00% 778,287 0.31% 147 0.00% 142,358 0.86%

2025 6,663 0.00% 1,749,761 0.65% 151 0.00% 276,325 1.63%

2030 4,177 0.00% 3,311,329 1.17% 159 0.00% 408,498 2.27%

Department of Energy | February 2012

Smart Grid System Report | Page 39

Figure 3.8. PHEV Market Penetration Scenarios

A number of other technologies are commercially available that take advantage of smart

grid features. For example, in 2008, EIA reported that 9,591 utility- or customer-owned DG

units were grid-connected, representing a total capacity of 12,863 MW (EIA 2008). In addition,

the EIA reported 12,262 dispersed generators (not grid-connected), representing 9,773 MW for

owners/operators of a distribution system [Metric 3–Distributed Resource Interconnection

Policy].

3.3.2 Enabling New Markets

A smart grid supports a more efficient allocation of resources through the use of

information systems enabling communication between the grid and “smart” appliances, DG

units, and other consumer-oriented devices. Further, a smart grid rewards customers who

engage in load-shifting behavior through the use of advanced meters, communication of real-

time usage and price information, and incentive structures. Market-based approaches are

expected to effectively manage these resources and reduce costs to consumers and service

providers.

The new products, services, and markets highlighted in this section depend on regulatory

recovery for smart grid investments. Historically, regulated electricity service providers have

been rewarded for investment in capital projects and energy throughput. That is, expanded

peak demand has driven the need for additional capital projects, which increase the rate base.

As energy sales grow, revenues increase. Both factors run counter to encouraging smart grid

PNNL (PHEV)

UC Berkeley (EV)

UMTRI (PHEV)EPRI (PHEV)

UC Berkeley (EV)

0.0%

10.0%

20.0%

30.0%

40.0%

50.0%

60.0%

70.0%

80.0%

90.0%

2000 2005 2010 2015 2020 2025 2030 2035 2040 2045 2050

UC Berkeley (EV)

UC Berkeley (EV)

EPRI (PHEV)

EPRI (PHEV)

EPRI (PHEV)

PNNL (PHEV)PNNL (PHEV)

UMTRI (PHEV)

BCG (EV)

US DOE (PHEV)

Year

Sh

are

s o

f L

igh

t D

uty

Veh

icle

Sale

s

ORNL (PHEV)

ORNL (EV)

Department of Energy | February 2012

Smart Grid System Report | Page 40

investments. Thus, regulatory frameworks can discourage energy efficiency, demand

reduction, demand response, DG, and asset optimization.

Rate adjustments can be encouraged through the policy of decoupling, which breaks the

link between the amount of energy sold by a regulated electricity service provider and the

revenue it collects [Metric 4–Regulatory Recovery]. Breaking this link ensures that electricity

service providers will recover the fixed costs approved by their regulatory commission,

including an approved rate of return on investment, regardless of sales volume. The most

common decoupling policies include:

• Full decoupling—An electricity service provider recovers the allowed revenue for the

difference in projected versus actual sales.

• Partial decoupling—An electricity service provider recovers some of the difference between

the allowed and actual revenue.

• Limited decoupling—An electricity service provider recovers a true-up cost only when actual

revenue deviates from allowed revenue for a specific reason (NREL 2009).

In addition to decoupling programs, Lost Revenue Adjustment Mechanisms (LRAMs), riders,

and trackers impose rate adjustments based on estimates of lost revenue from energy-

efficiency or supply-side management programs. When states decouple and/or impose LRAMs,

the link between energy throughput and revenue weakens, allowing electricity service

providers to recover fixed costs even though electricity consumption may decline due to the

impact of energy efficiency programs.

There are 13 states, including the District of Columbia, that currently have a revenue

decoupling mechanism in place (Figure 3.9), eight states with decoupling policies pending, and

nine states with LRAMs. States that have enacted decoupling policies since 2008 include

Hawaii, Idaho, Massachusetts, Nevada, Oregon, Vermont, and Wisconsin (IEE 2010a).

Department of Energy | February 2012

Smart Grid System Report | Page 41

Figure 3.9. Status of States with Decoupling or Lost Revenue Adjustment Mechanisms

(IEE 2010a)

Increased use of state energy savings goals, such as renewable energy efficiency portfolio

standards, have also influenced state regulatory commissions to expand financial incentives to

electricity service providers that invest in energy saving mechanisms, such as energy efficiency

programs that may leverage smart grid technologies. Performance incentives for electricity

service providers refer to regulatory standards enacted to compensate providers that invest in

efficiency technologies and programs. Figure 3.10 demonstrates that 21 states now have a

performance incentive in place with an additional 7 states having pending policies. Colorado,

Hawaii, Kentucky, Michigan, New Mexico, New York, North Carolina, Ohio, Oklahoma, South

Carolina, South Dakota, Texas, and Wisconsin have all approved incentives since 2008

(IEE 2010a). Due in part to the improvement in the regulatory climate, budgets for energy

efficiency programs in the U.S. grew from $2.7 billion in 2007 to $4.4 billion in 2009 (IEE 2010b).

The markets established through new energy technologies have gained increasing

recognition with private investors as venture capital firms have expanded their investments in

smart grid technology providers. This interest has been spurred on by several investment

drivers:

• high oil prices making energy delivery by electricity service providers more costly

• peak demand growing at a time when energy infrastructure is in need of updating and

replacement

• shrinking capacity margins

• increasing recognition of clean and efficient technologies.

Department of Energy | February 2012

Smart Grid System Report | Page 42

Figure 3.10. Improved Performance Incentive Programs (IEE 2010a)

These drivers suggest that in the future, new products, services, and markets will be

required to address the growing demand for energy over the long term. As a result, investment

in smart grid technologies has continued to gain traction. In 2009 alone, numerous significant

venture capital deals were announced:

• SynapSense received $7 million for the development of wireless energy-efficiency solutions

and data centers.

• Silver Spring, which is a wireless smart grid equipment and software developer, received

$15 million.

• Tendril Networks secured $30 million toward the development of smart grid software and

wireless sensors.

• Powerit Solutions received $6 million to support development of electric transformer cores.

• OutSmart Power Systems secured $2 million to develop hardware and software systems

designed to monitor and manage energy usage and other commercial building activities.

The surge in private sector investment was validated with venture capital data for the smart

grid market for 2000 through 2009 obtained from the Cleantech Group. The Cleantech Group’s

database includes detailed information at the company level. For each transaction, the amount

of the transaction, the name of the company, and the company’s focus were identified.

Transactions were stratified by year. Based on the data presented by the Cleantech Group,

venture capital funding secured by smart grid startups was estimated at $194.1 million in 2007

and $414.0 million in 2009, representing a two-year growth rate of over 113 percent (Fan 2008,

Department of Energy | February 2012

Smart Grid System Report | Page 43

Cleantech Group 2010). While significant, smart grid venture capital investment represented

only 7 percent of the total clean technology investment monitored by Cleantech in 2009. The

largest sectors for clean technology venture capital investment in 2009 included solar

($1.2 billion), transportation ($1.1 billion), energy efficiency ($1.0 billion), and biofuels

($554 million). In total, the Cleantech Group identified smart grid venture capital deals totaling

more than $1.6 billion during the 2000 through 2009 timeframe.

Data provided by the Cleantech Group were used to construct Figure 3.11. Annual venture-

capital funding levels are presented along with a two-period moving-average line. As shown,

venture capital funding of startups slumped between 2000 and 2002 but has since rebounded,

growing from $58.4 million in 2002 to $414.0 million in 2009. Between 2002 and 2009, venture

capital funding of smart grid startups grew at an average annual rate of 32.3 percent. While

growth in smart grid venture capital investment was robust during the 2002 through 2009 time

period, a cautionary note is needed as global investment in clean technologies, including smart

grid, dropped in the second half of 2010 with venture capital investment in the third quarter

down by 30 percent compared to the second quarter of 2010 and 11 percent compared to the

third quarter of 2009. In the fourth quarter of 2010, global investment in clean technologies

declined for the second consecutive quarter by an additional 17 percent compared to the third

quarter of 2010.

Figure 3.11. Venture Capital Funding of Smart Grid Startups (2002 through 2009)

$-

$50

$100

$150

$200

$250

$300

$350

$400

$450

Year

VC

Fu

nd

ing

of

Sm

art

Gri

d S

tart

up

s (

Millio

ns)

VC Funding 109.90 69.15 58.38 67.44 95.81 105.66 134.72 194.05 345.00 414.00

2000 2001 2002 2003 2004 2005 2006 2007 2008 2009

Department of Energy | February 2012

Smart Grid System Report | Page 44

Figure 3.12 breaks down venture-capital funding for the 2007 to 2010 time frame by the

type of services provided for each smart grid company in the Cleantech Group database. From

2007 through 2010, more than 50 percent of the venture capital spending in the smart grid area

went to metering companies (Figure 3.12). Home energy management companies received

20 percent of all venture capital spending and building energy management companies

received 18 percent during the 2007 through 2010 timeframe (Neichin and Cheng 2010).

Figure 3.12. Venture Capital Spending by Company Type (2007 through 2010)

Advanced metering technology is a key facilitator of new markets because of its ability to

record energy usage on very short time intervals [Metric 12–Advanced Meters]. AMI and other

communicating technologies can provide the means to communicate real-time pricing data,

grid conditions, and consumption information. More detailed data improves the flow of pricing

information to consumers, improves accuracy of demand forecasts, and enhances the ability of

the electricity service provider to respond to surges in demand. Further, the exchange of real-

time prices and market data allows consumers to effectively monitor their energy consumption

and respond to dynamic pricing tariffs. AMI penetration reached 7.95 million meters installed

nationwide in 2009. Projections for future installation of AMI range from a partial deployment

figure of 80 million meters installed by 2019 to 141 million under a full deployment scenario

(FERC 2009b).

Venture capital is only one source of R&D funding for smart grid companies. Public and

private agencies across the U.S. are increasingly investing in the development of smart grid

technologies. Since 2004, implementation of renewable portfolio standards, interest in energy

Department of Energy | February 2012

Smart Grid System Report | Page 45

efficiency and smart grid technology development have enhanced energy R&D budgets. One

study estimated global public investment in smart grid specific R&D programs during 2009 to be

$530 million, led by the U.S., Italy and Japan (IEA 2010ARRA allocated $3.4 billion to smart grid

matching grant programs, including $327 million to research, instrumentation, and laboratory

infrastructure development (DOE 2009b). Although ARRA funding was a one-time stimulus

package, DOE (2010b) released a congressional budget request for fiscal year (FY) 2011 in

February 2010, designating $39.3 million for smart grid R&D. ARPA-E, which was initially

funded through ARRA in 2009, will continue to be funded through the DOE in 2011. The

National Science Foundation (NSF) has also allocated funds in the FY 2011 budget for smart grid

projects. Divisions that will receive funding include the Directorate for Computer and

Information Science Engineering, which budgeted $29.36 million for all R&D, the Directorate of

Engineering, which allocated $120 million for a variety of projects, including smart grid

development, and the Division of Antarctic Infrastructure and Logistics, which will receive $2

million to build AMI and monitor energy consumption at the McMurdo station (NSF 2010).

In addition to federal spending, contributions to smart grid R&D include many nonprofit

organizations, products and service companies, electricity service providers and commissions.

One such organization is EPRI (2010), which has allocated $15.6 million to R&D projects taking

place in 2010. Projects include grid operations, planning, distribution, energy storage, demand

response, distributed renewable generation and PHEV grid integration. In addition, the

California Energy Commission (CEC)has appropriated $83.5 million to R&D annually as legislated

through Senate Bill 1250 (CEC 2010).

R&D projects include demand response, renewable energy development and advanced grid

technology research. Demand-response equipment also enables the design and function of

new markets. From a system operations point of view, demand response is sometimes viewed

as a form of additional capacity and is discussed in terms of MW. A 2008 FERC report estimated

that the potential generation reduction due to such demand-response programs is

approximately 41,000 MW per year. While the opportunity is significant, there is little

standardized supporting infrastructure to communicate with grid-responsive demand-side

equipment, nor is there significant demand for it yet since only approximately 8 percent of U.S.

energy customers now have any form of time-based or incentive-based price structure

(FERC 2008).

Microgrids represent another new smart grid-enabled market area [Metric 6–Load Served

by Microgrids]. A microgrid is a distribution system with distributed energy sources, storage

devices, and controllable loads that may generally operate connected to the main power grid

but is capable of being operated as an island. Microgrids could change the landscape of

electricity production and transmission in the United States due to the changing technological,

regulatory, economic, and environmental incentives. The changing incentives could allow the

modern grid to evolve into a system in which centralized generating systems are supplemented

Department of Energy | February 2012

Smart Grid System Report | Page 46

with small, more distributed production using smaller generating systems, such as small-scale

combined heat and power (CHP), small-scale renewable energy sources and other distributed

energy resources.

The cost of connecting and configuring smart devices and systems into the electricity grid

remains an obstacle to the high volume penetration levels anticipated. For automation

components to connect and work, alignment is needed in communication networks,

information understanding, business processing, and business and regulatory policy (see

Figure 3.13). This alignment results in interoperability and it is aided by integration methods

and tools, as well as adherence to standards and agreements that cover all these aspects

[Metric 19–Open Architecture/Standards]. Interoperability is a key element necessary to

enable smart grid applications, and requires active collaboration from all stakeholder groups.

Under EISA, NIST has “primary responsibility to coordinate development of a framework

that includes protocols and model standards for information management to achieve

interoperability of smart grid devices and systems (110 USC 1305).” In November 2009, NIST

formed the SGIP and encouraged smart grid stakeholders from all organizations associated with

electric power to establish this community and advance interoperability through goals, gap

analysis, and prioritized efforts designed to address the challenges to integration (Widergren

et al. 2010).

Following two stakeholder workshops, EPRI, which had been awarded a contract by NIST,

produced a Report to NIST on the Smart Grid Interoperability Standards Roadmap. The report

identified more than 80 current standards that might be applied or adapted to aid in meeting

smart grid interoperability or cyber security goals. Further, it identified more than

70 standardization gaps and issues that require further attention (NIST 2010).

Standards and openness are also advancing in terms of the layers of agreement that must

align. The GridWise® Architecture Council (GWAC) was formed to engage stakeholders and

create a maturity model that can define and evaluate the process for system-wide

interoperability (Widergren 2010). The Smart Grid Interoperability Maturity Model (SG IMM)

proposes three major categories that need to be aligned to achieve interoperability: technical,

informational, and organizational. Figure 3.13 represents a simplified version of the SG IMM,

and is illustrating the three framework categories and the general goals for each

interoperability issue (configuration and evolution, operation, security and safety). In addition,

the framework identifies eight interoperability categories and ten issue areas that cut across

the interoperability categories. This model will help stakeholders as they focus on specific

areas of concern.

Department of Energy | February 2012

Smart Grid System Report | Page 47

Figure 3.13. Interoperability Categories (Widergren et al. 2010)

3.4 Provides Power Quality for the Range of Needs

Customer requirements for PQ are not uniform across the residential, commercial and

industrial sectors. PQ issues can include voltage sags, lightning strikes, flicker, and momentary

interruptions. Customer needs for PQ range from those of data centers, which currently

require on-site uninterruptible power supplies, to industrial plants, which need continuous

power requiring dual distribution feeders and backup generation, to residential customers who

are only occasionally irritated by flashing digital displays after a momentary interruption.

Related Metrics

5, 6*, 7, 9, 11, 17*, 21

Currently, the transmission grid and distribution systems are interconnected and can

interact with each other. Examples of this would include an arc furnace or large motor causing

lights to flicker on the same or different transmission lines or distribution segments. PQ

problems at commercial customer facilities can affect nearby facilities on the same distribution

feeder or particularly on the same distribution transformer. Current regulatory oversight of

utilities focuses on power interruption, not PQ.

Smart grid implementations, along with new regulation, could allow service providers to

provide varying grades of PQ. Higher-power-quality services could be provided based on

location, such as via a particular distribution feeder, or at a “power quality park.” Controls

would be necessary to monitor and counteract problems arising from switching surges,

Department of Energy | February 2012

Smart Grid System Report | Page 48

lightning strikes, line faults or harmonic sources nearby. Providing these options would require

a supportive regulatory framework and an attractive product and service offering to interested

customers. In many PQ situations, service providers offer premises-based PQ devices; these are

usually placed at the service entrances and billed for on a monthly basis.

The options for enhancing PQ with a smart grid [Metric 17–Power Quality] cover a range of

technologies and service provider program approaches, including

• PQ meters,

• system-wide PQ monitoring,

• demand-response programs,

• storage devices (e.g., batteries, flywheels, superconducting magnetic energy storage),

• inverter technologies to correct fluctuations in the quality of power from intermittent

distributed renewable resources,

• new DG devices with the ability to provide premium power to sensitive loads,

• active control of voltage regulators, capacitor banks, and inverter-based DG and storage to

manage voltage and volt-amps reactive (VARs),

• remote fault isolation,

• dynamic feeder reconfiguration, and

• microgrids.

Future technology gains could greatly increase PQ while reducing costs associated with

interruptions and associated productivity losses. A loss of power or a fluctuation in power

causes commercial and industrial users to lose valuable time and money. This section of the

report focuses on PQ issues rather than power disruptions, which are covered in Section 3.6.

The Cost of Poor Power Quality

In the past, PQ incidents were often rather difficult to observe and diagnose due to their

short durations. The increase in digital and power-sensitive loads has forced us to more

narrowly define PQ. For example, ten years ago a voltage sag might be classified as a drop by

40 percent or more for 60 cycles, but now it may be a drop by 15 percent for five cycles

(Kueck et al. 2004).

Cost estimates of power interruptions and outages vary. A study prepared by Primen, Inc.

in 2002 concluded that PQ disturbances alone cost the U.S. economy $15 to 24 billion annually

(McNulty and Howe 2002). In 2001, EPRI estimated power interruption and PQ cost at

$119 billion per year (Primen 2001), and a 2004 study from Lawrence Berkeley National

Department of Energy | February 2012

Smart Grid System Report | Page 49

Laboratory (LBNL) estimated the cost at $80 billion per year (Hamachi LaCommare and Eto

2004). A 2009 NETL study suggested that these costs are approximately $100 billion per year,

and further projected that the share of load from sensitive electronics (microchips and

automated manufacturing) will increase by 50 percent in the near future (NETL 2009). The

range of costs for power interruptions can vary from $0.1 per kw in commercial businesses to

$60+ per kw of demand in semiconductor foundries. Detailed cost estimates can be found in

Table M.17.1 of Appendix A.

LBNL produced a report in 2009 that analyzed the results of 28 customer value-of-service-

reliability studies conducted by ten U.S. electric utilities between 1989 and 2005. Table 3.6,

which is not limited to momentary outages, summarizes the costs associated with types of PQ

disturbances (NETL 2009).

Table 3.6. Estimated Average Electricity Customer Interruption Cost Based on U.S. 2008 Dollars

by Customer Type and Duration

Interruption Duration

Momentary 30 Minutes 1 hour 4 hours 8 hours

Medium and Large Commercial & Industrial

Cost per Event $11,756 $15,709 $20,360 $59,188 $93,890

Cost per Average kw of Demand $115.20 $14.40 $19.30 $25.00 $72.60

Small Commercial & Industrial

Cost per Event $439 $610 $818 $2,696 $4,768

Cost per Average kw of Demand $2,173.80 $200.10 $278.10 $373.10 $1,229.20

Residential

Cost per Event $2.70 $3.30 $3.90 $7.80 $10.70

Cost per Average kw of Demand $1.80 $2.20 $2.60 $5.10 $7.10

Note: These cost estimates are those for interruptions occurring on summer weekday afternoons.

Smart Grid Solutions to Power Quality Issues

Smart grid devices serve a variety of functions, including “fault location, fault isolation,

feeder reconfiguration, service restoration, remote equipment monitoring, feeder load

balancing, Volt-VAR controls, remote system measurements, and other options” (Uluski 2007).

If operated properly, transmission and distribution automation systems can provide more

reliable and cost-effective operation through increased responsiveness and system efficiency,

all of which lead to improved PQ.

DOE’s SGIG program has funded a wide range of technology to add automation features to

the U.S. grid. The SGIG program is investing 3.4 billion dollars over 3 to 5 years. Substation

automation projects (Irwin 2010) are estimated to number 671, affecting 5 percent of the total

12,466 T&D substations in the U.S. [Metric 11–T&D Automation].

Department of Energy | February 2012

Smart Grid System Report | Page 50

Microgrids [Metric 6–Load Served by Microgrids] hold the promise of enhancing PQ and

improving efficiency, when successfully implemented. A microgrid is an integrated distribution

system with interconnected loads and distributed energy sources and storage devices, which

operates connected to the main power grid but is capable of operating as an island; it could be

as small as a city block or as large as a small city (Lasseter et al. 2002, Rahman 2008). Key

distinctions between a microgrid and distributed generation are the ability of the microgrid to

be islanded with coordinated control (Lasseter 2007). Table 3.7 shows federal funding

commitments made to developing microgrids (DOE 2010c, SGIC 2010).

Grid responsive load [Metric 9–Grid-Responsive Non-Generating Demand-Side Equipment]

measures the amount of load that can respond to signals (price or otherwise) and participate in

system operations. Grid responsive load can be aggregated to participate in demand response

programs.

Table 3.7. Federally Funded Micro Grid Projects

Project State City Description

Pecan Street Project

Energy Internet

Demonstration

TX Austin The recipient will develop and implement an energy internet microgrid

located in a large mixed-use infill development site in Austin, Texas.

San Diego Gas and

Electric Borrego

Springs Microgrid

Demonstration

CA Borrego

Springs

The recipient will install and operate Home Area Network devices on 125

homes (exact homes to be determined) in the Borrego Springs community.

It will also modify or replace equipment along existing electricity service

provider right-of-way. Finally, it will install and operate a utility-scale diesel

generator, batteries and related equipment, and components within the

existing Borrego Springs substation (DOE 2010c).

Allegheny Power WV Morgantown The project received $4 million in federal funding to demonstrate advanced

operational strategies such as dynamic islanding and microgrid concepts

and examine new ways to serve priority loads through the integration of

automated load control with advanced system control (SGIC 2010).

Illinois Institute of

Technology

IL Chicago The project received $7 million in DOE funds to develop an integrated

microgrid system capable of full islanding (SGIC 2010)

Demand response was defined by the U.S. DOE in its September 2007 report to Congress

(FERC 2007b):

“Changes in electric usage by end-use customers from their normal consumption

patterns in response to changes in the price of electricity over time, or to

incentive payments designed to induce lower electricity use at times of high

wholesale market prices or when system reliability is jeopardized.”

Examples of grid-responsive equipment, which could positively impact PQ issues, include

communicating thermostats, responsive appliances, responsive heating, ventilation, and air

conditioning (HVAC) equipment, consumer energy monitors, responsive lighting controls, and

controllable wall switches. This category of equipment also encompasses switches, controllable

power outlets and various other controllers that could be used to retrofit or otherwise enable

Department of Energy | February 2012

Smart Grid System Report | Page 51

existing equipment to respond to smart grid conditions. For example, a new “smart”

refrigerator may be equipped with a device that coordinates with the facility’s energy

management system to adjust temperature controls, within user-specified limits, based on

energy prices.

Various types of DG [Metric 7–Grid-Connected Distributed Generation] and energy-storage

equipment are connected to the grid and can range from backup generators, microturbines,

CHP systems, solar panels, wind turbines and a wide range of energy storage technologies that

include batteries, compressed air storage systems, flywheels, and ultra-capacitors. Unlike large

and centralized generators that provide most of the grid’s power, DG systems are noted for

their smaller-scale local power generation (10 MVA or less), and they can be connected to

primary and/or secondary distribution voltages (IEEE 2003).

Energy storage can improve PQ by storing energy during periods of low demand and

discharging energy back into the system during peak periods. Thus, it can function as a

generator with limited energy (during the discharging mode) and as a load (during the charging

mode).

Energy storage systems have the ability to:

• integrate intermittent renewable energy technologies

• act as an uninterruptable power supply, which is the electricity system equivalent of

providing ancillary services such as load following, regulation and spinning reserve

• defer upgrades to transmission and distribution infrastructure and provide an alternative to

inflexible, “lumpy” additions to transmission and distribution capacity.

Of the projects funded by ARRA, there are 37 with a combined value of $637 million that

combine smart grid and energy storage functionalities. Additionally, ARRA funding of

$2.4 billion is directed toward aiding vehicle battery and component manufacturers [Metric 8–

EVs and PHEVs]. The scaling up of battery production to meet demand generated through

PHEV sales could serve as an opportunity to reduce the cost per kilowatt of lithium ion batteries

and provide a new source of batteries in a secondary application to the grid. This could include,

but not be limited to, storage at the distribution transformer level, also known as community

energy storage. Vehicle-based battery systems offer an energy storage option that could

provide the equivalent of an uninterruptable power supply to a home or neighborhood, thus

increasing power reliability at the end-user level and improving PQ.

With advancement of energy storage systems, grid-connected renewable electricity

generation [Metric 21] could also serve to enhance PQ and has climbed from a little over

2 percent of total grid-connected electricity generation in 2005 to over 3.5 percent in 2010

(Figure 3.14). The increase in renewables generation resulted primarily from an increase in

Department of Energy | February 2012

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wind generation. Wind generation increased dramatically over the time period, from

approximately 18 gigawatt-hours (GWh) in 2005 to more than 70 GWh in 2010 (EIA 2010e).

Figure 3.14. Trends in Renewables as a Percent of Total Net Generation (EIA 2010e)3

Renewable energy capacity as a percent of total summer-peak capacity has grown by

almost 50 percent since 2004, increasing from just over 4 percent to almost 6 percent. The

penetration of intermittent renewables is indicated by net wind and solar summer capacity as a

percent of total summer-peak capacity. Currently, nationwide intermittent generation

comprises approximately 2.5 percent of total capacity (EIA 2010e). Nationwide wind capacity is

forecast to more than double by 2035, reaching 5.5 percent (EIA 2010d).

3.5 Optimizing Asset Utilization & Operating Efficiency

The premises of the smart grid concept are lower operations costs, lower maintenance

costs and greater flexibility of operational control than the current grid system exhibits. This

operational efficiency and improved asset utilization will be driven by advanced communication

and information technologies. Better monitoring and control technologies reduce the need for

additional generation plants and towers or transmission lines, and thereby reduce the need for

increased generation through demand response measures and energy efficiency.

This section reports on smart grid improvements in asset utilization and operating efficiency

in bulk generation, T&D delivery infrastructure, and distributed energy resources in the

electricity system. It concludes with an overall view of system efficiency.

3 The percent of net generation for 2010 is based on the first 3 months of net generation data for 2010.

0.00%

0.50%

1.00%

1.50%

2.00%

2.50%

3.00%

3.50%

4.00%

1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010

Related Metrics

2, 3, 5, 7, 11*, 13, 14*, 15*, 16*

3.5.1 Bulk Generation

The United States crept closer to its generation limits for at least the ten years preceding

2000 according to NERC data, but it sharply reversed that trend during the next five years and

returned to more conservative generation capacity factors. Relative

capacity factors are predicted for the next eight years. The trends in the data can be observed

in Figure 3.15, which points out the maximum and minimum capacity factors and years for each

of the three data sets. A capacity factor [Metric 14] is the fraction of energy that is generated

by or delivered through a piece of power

the amount of energy that could have been generated or delivered had the equipment

operated at its design or nameplate capacity during that interval. Capacity factors have

declined from the previous SGSR because of the economic downturn. Load dropped as

business activity declined, leaving a fairly constant generation

thus reducing the capacity factor. A rising capacity factor can be viewed as a positive measure

from the standpoint of asset utilization and the smart grid could

without a reduction in grid reliability.

Figure 3.15. Measured and Predicted Peak Summer, Peak Winter, and Yearly Average

Generation Capacity Factors in the U.S.

Generation in the United States has seen relatively steady

50 years, following rapid growth in the efficiency of coal power in the 1950s. Single cycle steam

Rankine plants (coal and nuclear) produce the vast majority of electricity in the U.S. These

plants, though not as efficient as some others, use relatively inexpensive fuels, are less capital

intensive than most renewable resources, and operate at much higher an

than renewables. The leveling off of coal efficiency rates since the 1950s suggests the

Department of Energy

Smart Grid System

The United States crept closer to its generation limits for at least the ten years preceding

according to NERC data, but it sharply reversed that trend during the next five years and

returned to more conservative generation capacity factors. Relatively constant generation

capacity factors are predicted for the next eight years. The trends in the data can be observed

ut the maximum and minimum capacity factors and years for each

of the three data sets. A capacity factor [Metric 14] is the fraction of energy that is generated

by or delivered through a piece of power-system equipment during an interval, compared to

amount of energy that could have been generated or delivered had the equipment

operated at its design or nameplate capacity during that interval. Capacity factors have

declined from the previous SGSR because of the economic downturn. Load dropped as

ness activity declined, leaving a fairly constant generation resource to serve a smaller load,

thus reducing the capacity factor. A rising capacity factor can be viewed as a positive measure

from the standpoint of asset utilization and the smart grid could enable higher capacity factors

without a reduction in grid reliability.

Measured and Predicted Peak Summer, Peak Winter, and Yearly Average

Generation Capacity Factors in the U.S. (NERC 2009a)

Generation in the United States has seen relatively steady efficiency rates in the last

years, following rapid growth in the efficiency of coal power in the 1950s. Single cycle steam

plants (coal and nuclear) produce the vast majority of electricity in the U.S. These

plants, though not as efficient as some others, use relatively inexpensive fuels, are less capital

intensive than most renewable resources, and operate at much higher annual capacity factors

than renewables. The leveling off of coal efficiency rates since the 1950s suggests the

Department of Energy | February 2012

Smart Grid System Report | Page 53

The United States crept closer to its generation limits for at least the ten years preceding

according to NERC data, but it sharply reversed that trend during the next five years and

ly constant generation

capacity factors are predicted for the next eight years. The trends in the data can be observed

ut the maximum and minimum capacity factors and years for each

of the three data sets. A capacity factor [Metric 14] is the fraction of energy that is generated

system equipment during an interval, compared to

amount of energy that could have been generated or delivered had the equipment

operated at its design or nameplate capacity during that interval. Capacity factors have

declined from the previous SGSR because of the economic downturn. Load dropped as

to serve a smaller load,

thus reducing the capacity factor. A rising capacity factor can be viewed as a positive measure

enable higher capacity factors

Measured and Predicted Peak Summer, Peak Winter, and Yearly Average

efficiency rates in the last

years, following rapid growth in the efficiency of coal power in the 1950s. Single cycle steam

plants (coal and nuclear) produce the vast majority of electricity in the U.S. These

plants, though not as efficient as some others, use relatively inexpensive fuels, are less capital

nual capacity factors

than renewables. The leveling off of coal efficiency rates since the 1950s suggests the

Department of Energy | February 2012

Smart Grid System Report | Page 54

limitation of the Carnot efficiency for large plants, while the increase in gas efficiency shows the

improvement from gas turbines, mostly because of greater use of combined cycle power plants.

Figure 3.16 shows how the efficiency of generators that use fossil fuels in the United States has

improved over time. The trends show a relatively low starting efficiency, with rapid increases

for most fuels.

Figure 3.16. Generation Efficiency for Various Fossil Fuel Sources over Time (EIA 2007b)

The capacity factor for the United States has remained nearly unchanged from 2006 to

2008, making only a marginal improvement. Table 3.8 summarizes peak, generation, and

capacity data and the resulting annual average national capacity factor measurements for 2006

and 2008, the most recent years for which data are available. On average, a little less than half

of the nation’s generation capacity is now used, but more than 80 percent of the nation’s total

generation capacity is used during summer peaks. Smart grid techniques should be able to

increase asset utilization, thus increasing overall capacity factors.

Table 3.8. Measured and Projected Peak Demands and Generation Capacities for Recent Years

in the U.S. (NERC 2009a) and Calculated Average Capacity Factors

2006 2008

Summer peak demand (MW) 789,475 755,614

Summer generation capacity (MW) 954,697 997,911

Capacity factor for Metric 14a, peak summer (%) 82.69 75.71

Winter peak demand (MW) 640,981 644,869

Winter generation capacity (MW) 983,371 976,258

Capacity factor for Metric 14a, peak winter (%) 65.18 66.05

Yearly energy consumed by load (GWh) 3,911,914 3,989,058

Department of Energy | February 2012

Smart Grid System Report | Page 55

Capacity factor for Metric 14a, average (%)* 46.08 46.13

*The average of the NERC (2006 and 2008) summer and winter capacities was used for this calculation.

3.5.2 Delivery Infrastructure

Data from electricity operators across the nation show a clear trend of increasing T&D

automation [Metric 11–T&D Automation] and increasing investment in these systems. Key

drivers for the increase in investment include operational efficiency and reliability

improvements that drive cost down and overall reliability up. The lower cost of automation

with respect to T&D equipment (e.g., transformers, conductors) is also making the value

proposition easier to justify. With higher levels of automation in all aspects of the T&D

operation, operational changes can be introduced to operate the system closer to capacity and

stability constraints. T&D automation encompasses a large set of technologies, including

SCADA technologies, remote sensors and monitors, switches and controllers with embedded

intelligence, digital relays, and a large number of other technologies used in the T&D

infrastructure. The general operating scheme of these devices is to gather real-time

information about the grid through communication and coordination with other devices,

process the information on site, take immediate corrective action if necessary, and

communicate results back to human operators or other systems.

Results of interviews undertaken for the 2010 SGSR report indicate that:

• 47.7 percent of the total substations owned by respondents were automated.

• 78.2 percent of the total substations owned by respondents had outage detection.

• 82.1 percent of total customers had circuits with outage detection.

According to a multi-year study of electric power utility capital expenditure budgets

conducted by Newton Evans, planned investment in T&D infrastructure grew in 2010 relative to

2008 and 2009 levels. More system operators were planning increases in T&D automation

investments (41to 43 percent) than those decreasing future expenditures (5 to 11 percent).

Smart grid initiatives were cited as being more important factors in stimulating increased

investments than either regulatory mandates or government stimulus programs, as seen in

Table 3.9.

Table 3.9. Rationale for Change in Investment

Year

Regulatory

Mandates

Smart Grid

Initiatives

Government

Stimulus

2010 45% 62% 41%

2011 42% 64% 41%

Department of Energy | February 2012

Smart Grid System Report | Page 56

DOE has announced that the SGIG program will fund the installation of 877 PMUs for near-

nationwide grid coverage by wide area measurement systems (WAMS) technology. This

number is over five times the current installed number (166) of networked PMUs

(Overholt 2010). Data sharing from the field and between control centers and reliability

coordination centers improves the true operational view of the system without which

engineering buffers are developed that allow for inaccuracy or unpredictable situations.

Advanced measuring devices such as synchrophasors help improve the situational awareness

and reduce the engineering margin [Metric 13–Advanced System Measurement].

Dynamic line ratings (DLR) [Metric 16], also referred to as real-time transmission-line

ratings, are a well-proven tool for enhancing the capacity and reliability of our electrical

transmission system. Modern dynamic line-rating systems can be installed at a fraction of the

cost of other traditional transmission-line enhancement approaches. One of the primary

limiting factors for transmission lines is temperature. When a transmission line current

increases, the conductor heats up, begins to stretch, and causes the power line to sag.

Allowable distances between power lines and other obstacles are specified by the National

Electric Safety Code (NESC).

A dynamic line-rating system can increase line transmission capacity by 10 to 15 percent by

feeding real-time data into the normal, emergency, and transient ratings of a line, which about

95 to 98 percent of the time results in a less-conservative, higher-capacity rating of the line

(Seppa 2005). The standard practice is to apply a fixed rating, which usually is established using

a set of conservative assumptions (i.e., high ambient temperature, high solar radiation, and low

wind speed), to a transmission line. In contrast, dynamic line ratings utilize actual weather and

loading conditions instead of fixed, conservative assumptions. In a particularly interesting

twist, transmission of wind energy might become enhanced by dynamic line ratings due to the

cooling effect of wind (Oreschnick 2007).

The number of locations where dynamic line rating equipment is installed is small,

monitoring only a fraction of the nation’s transmission lines. There are a few pilot projects

intended to determine the feasibility and reliability of dynamic line rating equipment operating

in real time. A smart grid demonstration project being conducted by the Oncor Electric Delivery

Company LLC is using 45 load-cell tension-monitoring units and eight master locations to

demonstrate that DLR can relieve congestion and transmission constraints, provide operational

knowledge, make sure that safety-code clearances are not broken, and quantify/identify any

operational limits. The New York Power Authority is conducting a demonstration project that

evaluates instrumentation and dynamic thermal ratings for overhead transmission lines. EPRI is

providing their Dynamic Thermal Circuit Rating (DTCR) software, which provides dynamic

ratings based on actual load and weather conditions. Real-time data will be provided using

temperature monitors, video sagometers and tension monitoring equipment (Mayadas-Dering

et al. 2009).

Department of Energy | February 2012

Smart Grid System Report | Page 57

The interviews of electricity service providers conducted for the 2010 SGSR revealed that,

on average, only 0.6 percent of respondents’ transmission lines were dynamically rated when

weighted by the number of customers served by each respondent.

3.5.3 Distributed Energy Resources

Smart grid applications, such as demand response [Metric 5–Load Participation Based on

Grid Conditions] and grid-connected DG [Metric 7], should also improve grid operating

efficiency by controlling load and adding localized resources when required. In order for this to

occur, favorable DG interconnection standards are needed [Metric 3–Distributed Resource

Interconnection Policy]. According to a 2008 FERC Survey, only about 8 percent of customers

have a time-based rate or are involved in some form of a demand response program. In

addition, the number of service providers offering load management and demand response

programs is small, with direct load control (DLC) and interruptible/curtailable tariffs listed as

the most common incentive-based demand response programs (FERC 2008) (see Table 3.10).

Table 3.10. Entities Offering Load-Management and Demand-Response Programs (FERC 2008)

Type of Program Number of Entities

Direct Load Control 209

Interruptible/Curtailable 248

Emergency Demand-Response Program 136

Capacity-Market Program 81

Demand Bidding/Buyback 57

Ancillary Services 80

Asset optimization and operating efficiency go hand in hand, especially in serving the

provider's most expensive loads, i.e., peak load. There is potential to use a combination of cost-

effective resources to meet system needs for summer-peak shaving, and demand response

programs could encourage consumers to be active in dynamic pricing programs. For example

electricity service providers could send price signals based on market conditions, and in turn,

end users would respond by pre-cooling buildings when energy prices are lower. As electricity

prices rise, because demand is increasing, thermostats could be set to higher or lower

temperatures to reduce load (this option is cheaper than turning on backup generation). Lastly,

if demand response and load control resources are exhausted, then prices rise further and DG

would be turned on to meet peak demand. The above scenario is essentially what the

California Independent System Operator (CAISO) does with operation of its demand response

program utilizing the Open ADR standard (PIER Demand Response Research Center, 2010).

Department of Energy | February 2012

Smart Grid System Report | Page 58

3.5.4 Overall System Efficiency

Once the electricity has been generated, the delivery process is much more efficient,

though the total quantity delivered means that even a small percentage loss represents a

significant dollar amount [Metric 15–Generation and T&D Efficiencies]. The overall efficiency of

the grid is depicted in a diagram from EIA (2009) that shows the energy losses associated with

each piece of the overall process of generating and delivering electricity (see Figure 3.17).

Generation efficiency is measured in terms of heat rate (Btu/kWh), or the ratio of delivered

electric energy to the chemical energy in the fuel input. Transmission and distribution

efficiency are measured by the line losses incurred in transporting the energy. The relative

importance of these two factors can be judged from Figure 3.18. Note that although the energy

lost to transmission and distribution is a small fraction of the Carnot-cycle losses, they are not

small in absolute terms, and are worth addressing.

Total T&D losses were 245.9 billion kilowatt-hours in 2008. EIA data show a total of about

4,000 billion kWh for the year, resulting in a 7.5 percent loss (see Figure 3.19). While the

efficiency number seems strong, the energy loss is equivalent to continuous generation of

28 GW, approximately the level produced by 29 large power stations.

Figure 3.17. Electricity Flow Diagram 2009 (Quadrillion Btu) (EIA 2009)

Figure 3.18. Combined Transmission and Distribution Efficiency over Time

EPRI has launched two new initiatives: one for improving the efficiency of the transmission

grid and the other for improving the efficiency of the distribution grid. DOE is also working on

related technology and recently awarded a $3.7 million grant to C

company that is developing high

electrical substations.

3.6 Operates Resiliently to Disturbances, Attacks, & Natural

Disasters

Resilience refers to the ability of a system

the system is restored to or is able to maintain normal operations. These “self

responses enable reduced interruption of service to consumers and help service providers to

better control the delivery infrastructure. Whether the disturbances are caused by an accident,

natural disaster or terrorist attack, a smart grid responds appropriately to maintain the grid’s

ability to function. A terrorist attack could be either from physical attacks upon th

infrastructure or from cyber-attacks

other signaling devices. Thus, a smart grid requires a balanced approach across all of its

elements at all stages to make sure that the entire system is protect

operating synchronously.

Operational resilience is arguably the most important characteristic of a smart grid from a

national security point of view. Resilience cuts across all the other characteristics of a smart

grid, regardless of adverse conditions or unforeseen events. Unlike some of the other metrics,

resilience and security are embedded in operational philosophies, processes, rules, and

vigilance. While it must be pervasive throughout the system, resilience is a less tangible qu

Resilience requires an ability to understand the system’s vulnerabilities, to quantify its risks,

Department of Energy

Smart Grid System

. Combined Transmission and Distribution Efficiency over Time

EPRI has launched two new initiatives: one for improving the efficiency of the transmission

grid and the other for improving the efficiency of the distribution grid. DOE is also working on

related technology and recently awarded a $3.7 million grant to Cree, a North Carolina

company that is developing high-voltage silicon carbide transistors for power management in

Operates Resiliently to Disturbances, Attacks, & Natural

Resilience refers to the ability of a system to respond to disturbances such that the rest of

the system is restored to or is able to maintain normal operations. These “self-

responses enable reduced interruption of service to consumers and help service providers to

ery infrastructure. Whether the disturbances are caused by an accident,

natural disaster or terrorist attack, a smart grid responds appropriately to maintain the grid’s

ability to function. A terrorist attack could be either from physical attacks upon th

attacks on the communications system either through internet or

other signaling devices. Thus, a smart grid requires a balanced approach across all of its

elements at all stages to make sure that the entire system is protected, integrated and

Operational resilience is arguably the most important characteristic of a smart grid from a

national security point of view. Resilience cuts across all the other characteristics of a smart

verse conditions or unforeseen events. Unlike some of the other metrics,

resilience and security are embedded in operational philosophies, processes, rules, and

vigilance. While it must be pervasive throughout the system, resilience is a less tangible qu

Resilience requires an ability to understand the system’s vulnerabilities, to quantify its risks,

Department of Energy | February 2012

Smart Grid System Report | Page 59

. Combined Transmission and Distribution Efficiency over Time (EIA 2009)

EPRI has launched two new initiatives: one for improving the efficiency of the transmission

grid and the other for improving the efficiency of the distribution grid. DOE is also working on

ree, a North Carolina-based

voltage silicon carbide transistors for power management in

Operates Resiliently to Disturbances, Attacks, & Natural

to respond to disturbances such that the rest of

-healing”

responses enable reduced interruption of service to consumers and help service providers to

ery infrastructure. Whether the disturbances are caused by an accident,

natural disaster or terrorist attack, a smart grid responds appropriately to maintain the grid’s

ability to function. A terrorist attack could be either from physical attacks upon the

on the communications system either through internet or

other signaling devices. Thus, a smart grid requires a balanced approach across all of its

ed, integrated and

Operational resilience is arguably the most important characteristic of a smart grid from a

national security point of view. Resilience cuts across all the other characteristics of a smart

verse conditions or unforeseen events. Unlike some of the other metrics,

resilience and security are embedded in operational philosophies, processes, rules, and

vigilance. While it must be pervasive throughout the system, resilience is a less tangible quality.

Resilience requires an ability to understand the system’s vulnerabilities, to quantify its risks,

Department of Energy | February 2012

Smart Grid System Report | Page 60

and to adjust approaches, operational techniques and postures through repeated evaluations.

Most of the metrics measuring progress of a smart grid’s advancement contribute to an

understanding of the progress for this characteristic.

The primary metrics of progress for this attribute include real-time data sharing [Metric 2],

grid-responsive non-generating demand-side equipment [Metric 9], T&D system reliability

[Metric 10], advanced sensors [Metric 13] and cyber security [Metric 18]. A number of other

metrics describe the system’s ability to operate resiliently to disturbances, attacks, & natural

disasters.

Related Metrics

1, 2*, 5, 6, 7, 8, 9*, 10*, 11, 12, 13*, 16, 18*

Given the great numbers of automation components interacting with a smart grid, an

important operational facet for the future smart grid is distributed decision making. That is,

equipment and smart grid subsystems need to share actionable information [Metric 2–Real-

time System Operations Data Sharing] so that local decision making not only serves local self-

interest, but collaboratively supports the overall health of the system. As individual

components of the system fail, including processing and communications components, the

remaining connected components have the ability to adapt and reconfigure themselves to best

achieve their objectives.

Operational resilience has three basic descriptive properties: 1) ability to adapt, expand,

conform, or contort when force is applied, 2) ability to perform satisfactorily or minimally while

the force is in effect, and 3) ability to return to an appropriate expected state once the force

stops or is rendered unproductive (Caralli 2006).

The state of smart grid deployment relevant to these properties is discussed in the following

section according to the major metric areas described in Section 2.1.

3.6.1 Area, Regional, National Coordination

At the transmission system level, area control centers and regional reliability coordination

centers have been exchanging system status information for many years now. These systems

are being continually upgraded to share more information including SCADA data, state

estimation results, and market data. The communication links between these systems now

cover the country and an increasing amount of information is being exchanged with

distribution-level systems.

A recent survey by Newton-Evans Research Company (2010) indicates there is significant

sharing of measurement, analysis, and control data from electricity service provider control

Department of Energy | February 2012

Smart Grid System Report | Page 61

systems for transmission and distribution SCADA, energy management systems (EMS), and

distribution management systems (DMS) with other grid entities including regional control

centers and other electricity operators. The survey was completed by over 100 utilities in the

U.S. and Canada, representing a total of 66,129,387 end-use customers (Newton-Evans 2010).

Utilities were asked to report the level of EMS/SCADA/DMS systems in place, and specify the

type of system. Results from the 2010 survey are represented in Figure 3.19.

Figure 3.19. Current Installations of EMS/SCADA/DMS Systems by Type (Newton-Evans 2010)

PMU technologies provide data necessary to measure phasor information in real time. A

benefit of this kind of measurement system is that the data are combined across a large area to

give a view of the overall power system operation. There is no single authority keeping track of

the deployment of PMUs. Therefore, the trends and projections given here were derived from

several sources. Projections of the number of PMUs required to adequately monitor the grid

vary from 500 to 1,300 PMUs based upon work by a NERC (2007) technical committee and a

study completed by Northeastern University, respectively (Galvan et al. 2008).

The number of installed and networked PMUs has been increasing steadily in the past few

years. The North American SynchroPhasor Initiative (NASPI) documented 140 networked PMUs

installed in the U.S. in 2009. In 2010, the number increased to 166 PMUs. ARRA investment is

expected to produce a significant increase in networked PMUs by 2014, with networked PMUs

reaching 1,043 (Overholt 2010). Figure 3.20 illustrates the growth of networked PMUs

between 2009 and 2014.

Figure

A transformational aspect of a smart grid

demand-side resources, into system operations. The ability of these resources to respond to

local area, regional, and national conditions contributes to economic efficiency and system

reliability. Market-based approaches encourage customers to use these and other system

resources to adapt to changing system conditions. The scalability and financial incentives tied

to such an approach support adaptation of the system to impending threats, disturbances, a

attacks. In particular, CPP and RTP programs provide a mechanism for system operators and

reliability coordinators to engage these resources to enhance operational resilience.

3.6.2 DER Response Metrics

A smart grid provides the flexibility to adapt to a changing mix of demand

including changeable load, dispatchable DG and storage, and variable

such as wind and solar. Demand

constraints or support electrically energized islands to mitigate the effects of events such as a

disturbance, attack, or natural disaster. In addition, these resources can improve response

times for post-interruption reconstruction.

Approximately 8 percent of customers have a time

of demand-response program [Metric 5

according to a 2008 FERC Survey. DLC and interruptible/curtailable tariffs are the mos

frequently listed demand response programs offered by electric service providers. However,

the number of organizations offering such a program is low. FERC estimates that in 2009, peak

Department of Energy

Smart Grid System

Figure 3.20. Number of PMUs Installed

A transformational aspect of a smart grid is its ability to incorporate DER, and particularly

side resources, into system operations. The ability of these resources to respond to

local area, regional, and national conditions contributes to economic efficiency and system

based approaches encourage customers to use these and other system

resources to adapt to changing system conditions. The scalability and financial incentives tied

to such an approach support adaptation of the system to impending threats, disturbances, a

attacks. In particular, CPP and RTP programs provide a mechanism for system operators and

reliability coordinators to engage these resources to enhance operational resilience.

DER Response Metrics

A smart grid provides the flexibility to adapt to a changing mix of demand-side resources,

including changeable load, dispatchable DG and storage, and variable-output local generation

such as wind and solar. Demand-response resources can help alleviate generating capacity

constraints or support electrically energized islands to mitigate the effects of events such as a

disturbance, attack, or natural disaster. In addition, these resources can improve response

interruption reconstruction.

roximately 8 percent of customers have a time-based rate or are involved in some form

response program [Metric 5–Load Participation Based on Grid Conditions],

according to a 2008 FERC Survey. DLC and interruptible/curtailable tariffs are the mos

frequently listed demand response programs offered by electric service providers. However,

the number of organizations offering such a program is low. FERC estimates that in 2009, peak

Department of Energy | February 2012

Smart Grid System Report | Page 62

is its ability to incorporate DER, and particularly

side resources, into system operations. The ability of these resources to respond to

local area, regional, and national conditions contributes to economic efficiency and system

based approaches encourage customers to use these and other system

resources to adapt to changing system conditions. The scalability and financial incentives tied

to such an approach support adaptation of the system to impending threats, disturbances, and

attacks. In particular, CPP and RTP programs provide a mechanism for system operators and

reliability coordinators to engage these resources to enhance operational resilience.

side resources,

output local generation

nerating capacity

constraints or support electrically energized islands to mitigate the effects of events such as a

disturbance, attack, or natural disaster. In addition, these resources can improve response

based rate or are involved in some form

Load Participation Based on Grid Conditions],

according to a 2008 FERC Survey. DLC and interruptible/curtailable tariffs are the most

frequently listed demand response programs offered by electric service providers. However,

the number of organizations offering such a program is low. FERC estimates that in 2009, peak

Department of Energy | February 2012

Smart Grid System Report | Page 63

demand reduction from demand response programs in RTO and ISO wholesale markets

amounted to 37 GW (FERC 2009b).

Distributed generation capacity [Metric 7–Grid-Connected Distributed Generation]

continues to be a small part of total power generation even though it has been steadily

increasing over the years. Total DG capacity reached 5,423 MW in 2004 and grew to

12,863 MW in 2008, or 1.7 percent of non-coincident summer peak (EIA 2010a). Wind and

other renewable energy sources grew significantly between 2004 and 2008, increasing by

1,051 percent, but that only represents 0.16 percent of total available generating capacity,

0.21 percent of summer peak capacity, and 0.24 percent of winter peak (EIA 2010c).

Intermittent renewable-energy resources such as wind may not be effective resource to meet

peak-demand, although solar has the potential to be more coincident with summer peak-

demand periods.

Other distributed energy resources are just now emerging. These include microgrids

[Metric 6–Load Served by Microgrids], which are designed to operate in both islanded and grid-

connected modes, and EVs, including PHEVs [Metric 8]. Microgrids were 0.08 percent of net

summer capacity in 2008 (EIA 2010a). EVs and PHEVs are a very small percentage of the market

presently and they do not currently contribute energy back into the grid.

In addition, non-generating demand-side equipment that responds to grid conditions and

pricing will also provide demand response [Metric 9–Grid-Responsive Non-Generating Demand-

Side Equipment]. The products that have emerged and continue to evolve in this category

either directly monitor or receive communicated recommendations from system operators.

The equipment then provides the useful dynamic responses to those needs either through

automated responses or through the conveyance of useful information to consumers who then

might appropriately respond. Smart grid-responsive appliances remain in their

commercialization infancy. However, programmable, communicating thermostats are a near-

term success in this equipment category. Numerous installations of communicating

thermostats have been conducted at pilot scale, and full-implementation installations are being

launched. For example, Austin Energy is presently evaluating the performance of 70,000

installed smart thermostats as part of its broader Pecan Street Project (FERC 2009b).

3.6.3 Delivery Infrastructure Metrics

Transmission and distribution substation automation projects and efforts to deploy

advanced measurement equipment for applications such as wide-area situational awareness

and dynamic line ratings are helping to improve the ability to respond resiliently and adapt to

system events.

The general operating approach for using SCADA technologies, remote sensors and

monitors, switches and controllers with embedded intelligence, digital relays, and a large

Department of Energy | February 2012

Smart Grid System Report | Page 64

number of other technologies is to gather real-time information about the grid through

communication and coordination with other devices, process the information on site, take

immediate corrective action if necessary, and communicate results back to human operators or

other systems. These devices serve a variety of functions, including “fault location, fault

isolation, feeder reconfiguration, service restoration, remote equipment monitoring, feeder

load balancing, Volt-VAR controls, remote system measurements, and other options”

(Uluski 2007). If operated properly, transmission and distribution automation systems can

provide more reliable and cost-effective operation through increased responsiveness and

system efficiency.

Two recent studies by LBNL on the cost of T&D reliability incidents provide insight into the

changing reliability of the grid. In 2004, electricity grid customers experienced 106 minutes of

interruption (SAIFI), 1.2 interruptions (SAIDI), 4.3 momentary interruptions (MAIFI), and

88 minutes of outage per incident (Customer Average Interruption Duration Index, CAIDI). By

2006 these numbers had increased significantly with 244 minutes of interruptions (130 percent

increase), 1.49 interruptions (24 percent increase), the number of momentary outages

increasing to 6.55 (51 percent increase) and the average outage almost doubling to

164 minutes (Table 3.11).

Table 3.11. Regional Statistics on SAIDI, SAIFI and MAIFI 2006

Census

Division

Sales as

Percentage

of Total IOU

Sales in

Region

Sales as

Percentage

of Total

U.S. Sales

in Region

SAIDI

(Minutes) SAIFI MAIFI

N Avg

Std

Dev N Avg

Std

Dev N Avg

Std

Dev

New

England 99% 68% 16 198 130 16 1.44 0.62 ND ND ND

Middle

Atlantic 100% 75% 21 225 188 21 1.28 0.55 ND ND ND

East North

Central 75% 62% 19 498 895 19 1.46 0.48 ND ND ND

West North

Central 57% 35% 12 166 202 12 1.31 0.68 2 5.11 5.03

South

Atlantic 71% 53% 18 320 200 18 1.86 0.62 4 11.1 2.16

East South

Central 0% 0% ND ND ND ND ND ND ND ND ND

West South

Central 88% 30% 18 134 56 18 1.38 0.46 ND ND ND

Mountain 35% 27% 7 118 58 7 1.22 0.54 ND ND ND

Pacific 99% 62% 12 296 214 12 1.99 1.21 6 3.40 2.35

U.S. 77% 58% 123 244 243 123 1.49 0.64 12 6.55 3.18

Note: N = number of reported values; Avg = average; Std Dev = standard deviation; ND = no data; IOU = investor-owned utility

Department of Energy | February 2012

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Due primarily to slumping commercial and industrial energy demand resulting from the

recent economic recession, forecasted peak electricity demand in NERC’s 2010 Long Term

Reliability Assessment was lowered 4.1 percent since the 2009 forecast, and 7.8 percent since

before the recession began (NERC 2010c). Presently, NERC forecasts an annual growth rate of

1.34 percent between 2010 and 2019, resulting in peak (summer) demand rising from 772 GW

to 870 GW (NERC 2010c).

Planning reserve margins demonstrate the forecast difference between peak electricity

demand and capacity. Figure 3.21 illustrates the forecasted reserve margins from 2009 to

2018. The rise in the near term reflects the current decrease in overall electricity demand.

Prospective systems are those planned or under construction, while deliverable systems

represent those already online. As the US economy recovers and demand rises, reserve

margins are forecast to decrease. Although reserve margins presented in this aggregated figure

are not forecasted to fall below the 15 percent NERC reference level, regional projections vary

significantly.

Figure 3.21. US Summer Peak Reserve Margin Forecast (NERC 2010d)

Deployments of T&D automation equipment and AMI indicate that distribution and

transmission providers are taking steps to improve declining margins. Data from electricity

operators across the nation show a clear trend of increasing T&D automation [Metric 11]

through increasing investment in these systems. Key drivers for the increase in investment

include operational efficiency and reliability improvements to drive cost down and overall

reliability up. The lower cost of automation with respect to T&D equipment (e.g., transformers,

conductors) is also making the value proposition easier to justify. With higher levels of

automation in all aspects of the T&D operation, operational changes can be introduced to

operate the system closer to capacity and stability limits. Recent research shows that while

Department of Energy | February 2012

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84 percent of electricity companies had substation automation and integration plans underway

in 2005, and about 70 percent of these companies had deployed SCADA systems to substations,

the penetration of feeder automation is still limited to about 20 percent (McDonnell 2006,

Moore and McDonnell 2007).

The capabilities of advanced meters will increase the accuracy of data for forecasts of price

and demand as well as provide the grid a better ability to respond to blackouts and brownouts.

With AMI, the grid can be more resilient. FERC forecasts a significant growth in AMI

deployment [Metric 12–Advanced Meters]. The number of smart meters grew from

approximately 0.9 million in 2006 to 6.7 million meters in 2008, 7.95 million in 2009

(FERC 2009b) and 16 million in 2010 (Neichin and Cheng 2010, King 2010), as represented in

Figure 3.22.

Figure 3.22. AMI Market Activity Growth

Federal grant awards for AMI implementation under ARRA total $812.6 million to date

(DOE 2010a). States with the most significant AMI investment under ARRA include Texas,

Maryland, Maine and Arizona, but projects are being undertaken by numerous electricity

service providers in 19 states, with additional laws or policies passed in Hawaii, Massachusetts

and Pennsylvania (FERC 2009b). Finally, since 2009, AMI pilots or full-deployment programs

have been announced by 26 utilities in 19 states (FERC 2009b).

DLRs [Metric 16] enhance the understanding of real-time transmission line capacity. A

standard practice is to apply a fixed rating, which usually is established using a set of

conservative assumptions (i.e., high ambient temperature, high solar radiation, and low wind

speed), to a transmission line. In contrast, dynamic line ratings utilize actual weather and

loading conditions instead of fixed, conservative assumptions. One study found that by feeding

0

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4

6

8

10

12

14

16

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2006 2008 2009 2010

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real-time data into a DLR system, the normal, emergency, and transient ratings of a line can be

continuously updated, resulting in a less-conservative, higher-capacity rating of the line about

95 to 98 percent of the time and increasing capacity by 10 to 15 percent (Seppa 2005). Another

study conducted by San Diego Gas & Electric found that monitored transmission lines had 40 to

80 percent more capacity than the static measurement of the line. These findings suggest that

technologies such as DLR systems could be adopted to address capacity shortcomings if growth

projections for transmission lines are not realized. Although DLRs are an important smart grid

technology, there is no publicly available nationwide data on them at this time.

3.6.4 Secure Information Networks

The interconnected North American grid is arguably the world’s largest and most complex

machine. It has an enviable record of reliability through the application of numerous

technological and operational efficiencies, and strong regulatory oversight. The grid’s

complexity and interconnected nature, however, pose a significant drawback; under the right

circumstances, problems occurring in one area have the potential to cascade out of control and

affect large geographical regions.

The security challenges, including cyber security, resulting from the interconnected nature

of the communication systems that support regional and interregional grid control and the mix

of newer systems with older legacy systems constitute a significant hurdle to overcome.

However, increased automation and intelligence in the field provided through microcontrollers

and sensors will enable enhanced monitoring capabilities in field equipment installed in

substations, within the distribution network, and even in customer’s homes and businesses. All

of these items, while posing security challenges, lead to significantly increased control

capability and vastly increased flexibility and functionality.

Steady progress has been made on the development of cyber standards, their

implementation and enforcement. The NERC CIP standards establish minimum requirements

for cyber security programs protecting electricity control and transmission functions. In early

2008, FERC directed NERC to tighten the standards even further to provide for external

oversight of classification of critical cyber assets and removal of language allowing variable

implementation of the standards. Version 3 revisions await FERC approval (NERC 2009c) while

Version 4 of the standards was being reviewed by the drafting team after receiving comments

during the informal comment period (NERC 2008).

The (Revised) Implementation Plan for Cyber Security Standards CIP-002-1 through CIP-009-

1 provides the implementation schedule for standards (Table 3.12) (NERC 2009d). CIP 002-4

has now become CIP 010-1, and CIP-003-4 through CIP-009-4 were consolidated into CIP-011-1

(Table 3.13) (NERC 2008).

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Table 3.12. Summary of the NERC Critical Infrastructure Protection Standards CIP 002-009

(NERC 2010a)

NERC Standard Subject Area

CIP-002 Critical Cyber Asset Identification

CIP-003 Security Management Controls

CIP-004 Personnel & Training

CIP-005 Electronic Security Perimeter(s)

CIP-006 Physical Security of Critical Cyber Assets

CIP-007 Systems Security Management

CIP-008 Incident Reporting and Response Planning

CIP-009 Recovery Plans for Critical Cyber Assets

*bulk electricity system (BES)

Table 3.13. Summary of the NERC Critical Infrastructure Protection Standards CIP 010-011

(Emerging) (NERC 2008)

NERC Standard Subject Area

CIP -010 BES Cyber System Categorization

CIP -011 BES Cyber System Protection

BES Cyber System Categorization

The implementation schedule identifies when entities must:

• begin work to become compliant with a requirement,

• be substantially compliant with a requirement,

• be compliant with a requirement, and

• be auditably compliant with a requirement.

The implementation schedule requires all BAs, TOs, and RCs to be auditably compliant with

NERC’s Urgent Action Cyber Security Standard 1200 (UA 1200) by December 2010. There were

different dates for each entity based upon their individual characteristics, but all types had to

be auditably compliant by the end of 2010.

Enforcement of the standards has identified a lack of compliance and therefore violations.

Identified violations are being reported according to the date on which the violation was found

to occur. From 306 CIP violations in July 2008, the number of CIP violations decreased to 54 in

January 2010 and continued to decline through May 2010 (Figure 3.23). As violations are

found, they increase past values based on the date the violation was deemed to occur. Thus,

the enforcement of standards and the subsequent corrections will over time lead to fewer and

fewer violations as companies take steps to come into compliance.

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Smart Grid System Report | Page 69

Figure 3.23. Deemed Date Trend for Active and Closed Violations (NERC 2010b)

Additionally, the interviews of 20 electricity service providers (Appendix B) included a

question about specific security measures that utilities are implementing. The results are

shown in Table 3.14.

Table 3.14. Security Question from Electricity Service Provider Interviews

Have you deployed the following security

features? (Select all that apply) Affirmative Response

a. Intrusion detection 62.5%

b. Key management systems 50.0%

c. Encrypted communications 66.7%

d. Firewalls 91.7%

e. Others 12.5%

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4.0 Challenges to Deployment

Significant challenges still exist for bringing the smart grid concept to fruition. Foremost

among these challenges is the value proposition and the capital required to purchase the new

technologies envisioned for communicating information between end users, electricity service

providers, and distribution and transmission providers. Although progress has been made in

the purchase and installation of PMUs and AMI deployments, estimates for advanced metering

capital still range up to $40 billion despite the billions of dollars provided for smart grid funding

in ARRA (Faruqui and Sergici 2009). With ARRA investments made in PMUs, the grid will be

approaching the target level of PMUs anticipated to adequately monitor the grid

(Overholt 2010).

Other areas of the grid including transmission, distribution, and software requirements will

also require capital investment to update the grid. Entrepreneurs who can find the appropriate

value propositions could lessen these significant capital requirements. However, with the rapid

changes in technology, energy mixes, and energy policy, the path to a smarter grid system

provides a degree of uncertainty that may not clearly signal large returns on capital for

investors. Yet, if legislators and regulators try to legislate or regulate the smart grid’s

development in order to provide certainty to investors, they could inhibit technology

innovations that might provide a more flexible and transparent energy market. Nevertheless,

allowing the grid to develop in a free market environment without some guidance could put the

energy grid and national interests at risk. For example, a failure to find a balance between

regulation and the free market was demonstrated in the Pacific Northwest in the spring of 2011

when wind farm operators were turned off the grid. Too much electricity was generated for

transmission equipment to accommodate and demand from electricity markets to consume

because of the convergence of spring winds and large melting snow packs increasing hydro and

wind electricity generation. In this case, there was an inability to address what happens when

too much power enters the grid, leading to questions such as who must shut down when it

occurs and who is responsible for ensuring the required distribution capacity is available to

meet peak supply. Thus, the challenge is to balance enabling innovation while at the same time

providing some certainty to the markets.

Technical progress is being made in the area of reliability and cyber standards that will

protect the grid from major failures like the 2003 blackout of the Northeast but further

progress is still needed. Significant progress has yet to be made in handling intermittent

generation or valuing storage technologies and making them cost effective. Storage is one

approach that could smooth generation from renewable resources such as wind and solar.

Studies indicate that when variable renewable resources reach 20 percent penetration, they

will be difficult and expensive to manage (Pratt et al. 2010). The software and hardware

required to allow this integration are making significant progress, but the integration of these

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programs is still in the demonstration stage. Lastly, regulations are required to update

reliability standards, encourage interoperability, establish favorable interconnection standards,

and enable investment recovery for justified smart grid investments. These and other

challenges are discussed in the following sections.

4.1 Technical Challenges

There are a number of technical challenges facing the smart grid associated with

implementing demand response, monitoring and communicating loads and prices, and

developing the standards required to ease the integration of these resources as participants in

system operations. Among these challenges is the definition of the interoperability

requirements that allow both sides of the meter to communicate with one another and to

adjust consumption patterns based on real-time pricing, as well as the development of

computer hardware and software, and communications infrastructure to handle dynamic

pricing and AMI, developing appropriate interconnection standards to implement intermittent

renewable energy resources, and standardization of communication protocols for demand-side

management appliances. Lastly, more standardized codes, requirements and reporting of T&D

reliability are needed (FERC 2007a).

Although AMI deployment has increased since 2008, there is still a lack of smart-meter

infrastructure and a lack of interoperability and open standards (Kaplan 2009). In addition, the

data infrastructure improvements required to collect and support the necessary data

accessibility need to be developed. National smart-meter readings are estimated to require

57.3 petabytes of data storage per year (Miller 2009). Other technical challenges include

developing approaches to ease integrating metering equipment, smart appliances and the

associated communication equipment linking the various parties involved in electricity

transactions (Callahan 2007). Other hardware challenges for smart appliances include

accommodating diverse operating environments such as temperature extremes and water

exposure (Gabriel 2009).

A streamlined integration of DG and storage devices, as well as microgrids, requires well

understood system interconnection agreements that can be replicated safely and easily.

Perhaps the largest technical hurdle in moving toward a smart grid is accommodating

intermittent renewable energy resources and overcoming the associated effects on grid

stability; even scale and dispersion will not overcome the variability in power production. As

such, innovations in storage technologies currently under development will need to become

cost effective and commercialized (Kreutzer et al. 2010). The California ISO noted in their draft

report on wind integration that energy ramps as high as 3,000 MW per hour or more were

possible during summer peak (CAISO 2007). Until storage technologies are developed that can

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be shown to be cost effective, control methods at the transmission and distribution levels are

extremely important.

The integration of DER makes fault detection, planning and design, and voltage regulation

more difficult (Pai 2002). Voltage-regulation challenges include overvoltage issues, which can

arise due to the two-way flow of electricity in the distribution system (Eynon 2002). Driesen

and Belmans (2006) point out that DER will present technical hurdles in terms of frequency,

voltage level, reactive power and power conditioning. DG, microgrids, and storage resources,

including EVs and PHEVs, share similar monitoring and control challenges identified for

demand-response metrics. The system interfaces associated with incorporating DG resources

widen significantly from the traditional grid interface. Lastly, PHEVs, EVs, fuel cells, wind

turbines, photovoltaics and batteries require inverters. The challenge is to bring the sources

online and maintain system voltage and frequency as the inverters used to transform power

from direct current (DC) generation units to alternating current (AC) power can increase

harmonics in the grid (Eynon 2002).

There are additional technical barriers for EVs and PHEVs, which include those related to

battery technologies, the automotive manufacturing process, limitations in range, refueling, the

supply chain, and electricity infrastructure capacity. Battery technology limitations include

energy intensity, durability, life span, size and weight, aspects of battery safety, the cost of

manufacturing, intellectual property issues, and raw-material constraints. While this challenge

continues today, EV charging stations are being installed across the U.S. and by 2015, Pike

Research (2010) estimates that nearly 1 million charging stations will be in place in the U.S. with

4.7 million available worldwide. In comparison, there were approximately 159,000 retail

gasoline outlets located in the U.S. in 2010 (National Petroleum News 2010).

To enable higher capacity utilization of existing transmission lines, limiting factors such as

voltage instability and transient stability, which can significantly affect transmission-line

transfer capacity more than the thermal limitations, must be monitored using DLR. Mayadas-

Dering et al. (2010) indicate that primary among DLR challenges are: educating asset-

management and operations personnel in the technical aspects of DLR to gain better

acceptance of the accuracy of the dynamic ratings; DLR rating variability; availability and

reliability of communications links to SCADA from remote substations; and instrumentation

reliability in light of the vulnerability of overhead lines to extreme weather conditions.

NETL identified specific challenges including developing premium-power programs (such as

setting aside specific office parks and areas for premium-power usage), developing storage

devices (such as superconducting magnetic energy storage) to supply ultra-clean power to PQ

sensitive consumers, and deploying DG devices capable of providing clean power to local

sensitive loads. Specifically, this requires technologies with the ability to identify and correct

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the failures that result in PQ issues, such as dynamic voltage restorers, static compensators, and

thyristor controlled static capacitors (NETL 2009).

4.2 Business and Financial Challenges

Some of the more important business and financial challenges include obtaining the capital

to finance smart grid project deployments, followed by the ability to recover costs, customer-

perception barriers, improving reliability, and improving power quality. A recent NERC survey

of industry professionals ranked aging infrastructure and limited new construction as the

number one challenge to reliability—both in likelihood of occurrence and potential severity

(FERC 2007b). With a smart grid, entrepreneurs may find the capital or solutions through

innovation that reduce the required capital but the ability to recover costs is an important

issue. Investment in smart grid projects is made more difficult when much of the data about

costs and revenues are based only on research rather than field deployments. Some of the

innovations to reduce the capital requirements may include AMI and the use of DG. In order

for the investments to occur, favorable interconnection standards that allow for appropriate

cost recovery must be developed; otherwise investments will not be made in renewable energy

production, DG, microgrids and demand response equipment. Additionally, approaches need

to be developed to share data to improve the use of phasor data and improve reliability. Lastly,

studies need to be developed that show the benefits of PQ devices.

There are also issues surrounding capital availability in regulated versus unregulated

markets. While electricity service providers operating in regulated markets tend to have

greater access to capital due to the higher expectation of return, they may not have the

incentive to invest in the smart grid because smart grid investments are likely to achieve

efficiency gains and thereby reduce their revenue. If, on the other hand, regulators allow

service providers to recover their investment through higher rates, consumers must bear the

burden of higher costs. Alternatively, unregulated markets have greater volatility with the

higher potential for default and thus face credit rationing with higher costs of capital; however,

innovators who can demonstrate the ability of a technology to yield a positive return on

investment can often find willing investors. Thus, there is a tradeoff in willingness to invest

between regulated and unregulated markets.

Even though ARRA allocated billions of dollars to smart grid technology, there are still

significant up-front costs to implement AMI. These include “labor costs associated with

deployment and installation of new meters, customer education, and IT system integration

costs” (Faruqui et al. 2009). Based on a California projection, implementing smart meters has

been forecast to cost as much as $40 billion (Faruqui and Sergici 2009). The projected costs will

probably increase due to varying regional requirements for AMI system features. Problematic

for AMI technology are drive-by or walk-by meters (automatic meter reading or automated

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meter reading [AMR]) that have existed for some time and are “possibly discouraging the

installation of the more demand response friendly AMI” (FERC 2007a).

Investments in AMI, which improves dynamic pricing, hinge upon energy and service

providers being able to recover their investments. Service providers incur significant costs

when installing AMI and updating billing systems. To date, regulatory recovery of these costs

has been a contentious issue. Furthermore, the regulated retail market can be a challenge for

third-party electricity aggregators and service providers who desire access to customers and

dynamic-pricing markets that can support viable business plans. In addition, until the value

proposition can be demonstrated to retail customers, the responsiveness of end users will be

limited and thus limit the cost recovery potential of both aggregators and service providers.

That is, consumers need to experience cost savings in order to support smart grid deployment.

If smart grid devices cost more than the offsetting value of reduced energy consumption or if

the savings are not well defined or understood, consumers may be unwilling to invest in them.

Without an expectation of buy-in from consumers, innovators and service providers may also

be reluctant to invest in smart grid technologies.

Quantitative assessments measuring customer responsiveness to prices are limited and

investors have seen participation in most voluntary RTP programs decline in recent years. In

part, the decline may be because without automated agents or controllers, consumers have to

spend too much time to monitor prices in order to reduce energy costs. However, with

installation of automated controllers or automated agents, customers could anticipate and take

advantage of price changes to reduce their energy costs with little effort. There may still be

much uncertainty about what price level would be enough to draw consumers to dynamic

pricing simply because consumers must find it worthwhile to take the extra effort to set up

their system to take advantage of dynamic pricing. Longer duration studies are needed that

evaluate the quality and quantity of data, and the price levels needed to entice consumer

response. Lastly, , insufficient market transparency, has been identified as key barriers to

developing functional demand response programs (FERC 2006).

In order to employ the full potential of DER, states may need to expand their existing laws

or institute new ones that allow the flow of surplus energy back to the grid (Kaplan 2009).

Barriers to DER interconnection may begin to diminish as more states adopt progressive

policies to allow higher penetration of DER. Barriers will remain in certain regions such as the

Southeast, where adoption of interconnection standards has been slow. Utilities have

commonly raised barriers to interconnection and self-generation and also discourage energy

efficiency investments because of the significant likelihood of a loss of revenue and profits with

lower demand for power (Venkataramanan and Marney 2008).

The challenge for states is to implement the recommendation in Section 1307 of EISA to

have utilities undertake a business analysis of non-advanced grid technology with alternative

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qualified smart grid technology investment. State implementation of the recommendation may

encourage shifting some of the large traditional investments to smart grid investments.

Education programs need to be developed that identify the costs and benefits of smart grid

programs and the associated laws and regulations that need to be developed to provide for the

associated recovery of smart grid investments. In addition, outreach programs need to be

developed for other stakeholder groups to educate them on the aspects of the smart grid that

pertain to their group. As an example, end users need to be educated on what information will

be provided to them from the smart grid, and how they will control the use of their electric

devices. These programs will provide the value proposition of the smart grid to regulators,

legislators, end users, and any other relevant groups.

Interconnection requirements must be resolved through standards such as IEEE 1547.4;

otherwise seamless transitions will not occur (NC 2005). Completion of the IEEE 1547.4

standard for microgrid requirements is also needed (IEEE 2008). Based on interconnection

standards measured by IREC and the NNEC, 13 states have policies favorable to grid

interconnection, 15 states have neutral policies and 22 states (including those with no

standard) have unfavorable policies (NNEC 2009).

On the generation side, making the grid compatible with DG, microgrids, and storage

systems could be expensive for system operators with limited ability to recover their costs.

System operator investment in equipment that integrates DG, microgrids, and storage systems

is complicated by the fact that the amount of energy transmitted by many of these

technologies is often unknown. Therefore, investment recovery can be limited and uncertain.

Service providers use standby charges to pay for natural-gas and other systems that stay on

standby when intermittent DG and microgrid generation are not available. These charges for

rarely-used infrastructure are a significant economic barrier to DG and microgrid deployments

(Hatziargyriou 2008, Venkataramanan and Marney 2008). In addition, electricity service

providers need to invest in instrumentation and communication to make the DG resources

dispatchable so that local service providers and transmission operators can deal with all the

technical issues associated with intermittency and control (DOE 2008).

While much progress has been made to integrate phasor data to improve reliability

including software and tools into reliability and utility settings, challenges still remain for

implementation at the research, planning and operational levels (Steklac 2007). In order for a

smart grid to function, many different entities need to communicate and share data on load

control, prices, and other elements. Yet, there are procedural, business and privacy issues that

hinder sharing of data and information collected by an electricity service provider with peers

and higher-level grid reliability coordinators. Circumstances may require sharing of information

with non-grid entities such as emergency response centers or state and federal government

agencies charged with public safety, homeland security, or national defense; however, data

sharing could provide operational and market intelligence to competitors that could be used in

service-area and/or corporate takeovers. The data could also be used by governments and

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regulators to second-guess operational decisions and reduce incentives or assess penalties for

outages and/or unsafe operating conditions. Data sharing also generates concerns about the

security of the shared information. These inhibitions are particularly significant when

operational data must be shared among peers, particularly local service providers and BAs.

Approaches are needed to remove these limitations, or data sharing will be limited and in turn

limit the efficiency, flexibility, and transparency of required communications.

Another challenge for the smart grid is to encourage the introduction of privacy enhancing

technologies into the design of smart grid technologies (Cavoukian 2009). Privacy issues are

important to all stakeholders. Privacy technologies that enable the transfer of information

while protecting the privacy of the provider and the user could significantly improve the

willingness of entities to provide and use the information required to enable a smart grid.

Cost effectiveness can inhibit some smart grid investments either because the benefits do

not accrue to the owner or because the cost effectiveness has not been shown to date. For

example Seppa (2004) notes that a significant business barrier to acceptance of dynamic line

ratings which could increase transmission capacity is the presence of societal benefits that

don’t necessarily accrue to the investor. Dynamic ratings technology benefits the whole

system, but the investors don’t necessarily obtain benefits in accordance with their costs. Thus,

system pricing needs to ensure that benefits accrue to the investor so that investments that are

beneficial to the system allow for their recovery. Microgrids are another example of where

they can mitigate many of the issues of two-way power flow, such as intermittency of

renewable resources. This may need regulatory support to occur. Another relevant case

involves advanced PQ devices. PQ devices are used by distribution companies to monitor and

diagnose problems. The PQ devices used, as well as those used by the end user, depend on the

size and type of the critical load. PQ-enhancing devices are still too expensive to be widely

used. Additional cost/benefit studies could provide a more complete accounting of the full

range of cost and benefits to the U.S. economy resulting from improving PQ (NETL 2009).

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5.0 Recommendations for Future Reports

Smart grid technologies have continued along their development and deployment paths

since the completion of the 2009 SGSR. Aided significantly by the passage of ARRA, recent

investment levels in smart grid applications have expanded rapidly and are expected to

continue their growth trends in the near future. Visions of an electricity system that not only

provides service but also interacts and communicates with end users, including generation and

storage in industrial plants, commercial businesses, and residential homes, are driving new

business and policy models as well as advancements in the technologies that support them.

Both the build metrics and the value metrics included in this report were designed to provide

insight into recent smart grid developments and deployments while identifying challenges from

both business and technical perspectives. While the research team has conducted an extensive

search for data to directly measure deployment trends for each of the 21 metrics explored in

this report, not all data gaps have been addressed and study shortcomings persist. The

remainder of this section discusses recommendations for future reports that could address

some of these shortcomings.

As the second in a series of biennial smart grid status reports required under EISA,

information gathered for this document should be used in conjunction with the 2009 SGSR to

form a framework and measurement baseline for future reports. The metrics identified in this

report are indicators of, and in some cases proxies for, smart grid deployment progress. They

are not comprehensive measures of all smart grid concerns; thus, these metrics should be

reviewed in subsequent reports for continued relevance and appropriate emphasis of major

smart grid attributes.

A recent report released by the National Science and Technology Council, A Policy

Framework for the 21st

Century Grid, emphasizes the importance of electricity customers being

able to access their own energy data in machine readable formats as a way to spur behavioral

energy efficiency as well as the development of new products and services, including greater

building and appliance automation (National Science and Technology Council 2011). To track

progress in this area, future reports may include a metric that measures consumer access to

energy data in machine readable formats.

Metric 21 – Grid-Connected Renewable Resources was added to the report to measure the

implementation of renewable electricity generation and the impact of those resources on

national CO2 emission levels. Future reports may include additional metrics, or it may be found

necessary to alter existing metrics for technologies that never reached full deployment levels or

were rendered obsolete by other technologies. However, care should be taken to avoid the

tendency to proliferate the number of metrics or undertake extensive changes from one report

to the next. In deciding whether a new metric is merited, consideration should be given to how

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it fits with the other metrics, whether a previous metric can be retired, and the strength of a

metric’s contribution to characterizing smart grid progress.

The nascent stage of many smart grid applications presents a number of measurement

challenges. As these technologies, deployments, and policies continue to evolve, so should the

SGSR and the metrics contained within it. While it is possible that future additions will be made

in terms of the metrics examined in the SGSR, the overall time series of smart grid

measurements that began in 2009 should remain as constant as possible in order to most

accurately reflect changes within the industry and to enable consistent measurement of

progress over time. As technologies evolve, some metrics that were initially identified as

nascent, such as Metric 6 – Load Served by Microgrids, may begin to play a more significant

role. As these metrics rise in significance, the manner in which they are measured may need to

change to reflect a better understanding of the technologies’ applications and capabilities.

Other metrics, such as Metric 12 – Advanced Metering Infrastructure, may level off after full

penetration. In order to recognize these traits, the SGSR should be monitored to determine the

status and classification of each metric.

Due to the early stages of development and deployment of some systems, many

technologies and standards evaluated in this report lack concrete definitions, causing

complications and potential discrepancies in data measurements. One example, Metric 9 –

Grid-Responsive, Non-Generating, Demand-Side Equipment, presents this challenge because of

questions surrounding specific equipment definitions and methods for measuring deployment

trends. Another metric with definitional questions is Metric 4 – Regulatory Recovery for Smart

Grid Investment. To address this and other similar issues present in other metrics, future

examiners may wish to work with industry and regulatory bodies to identify and provide more

concrete definitions for these metrics, thus enabling a more uniform basis of measurement.

In addition, for some of the metrics, there are incomplete or no data available for

quantitative analysis. Such difficulties are present in Metric 6 – Loads Served by Microgrids and

Metric 9 – Grid-Responsive, Non-Generating, Demand-Side Equipment. These difficulties could

cause inconsistencies in future reports. While obtaining additional information on all metrics is

a primary focus during the completion of the SGSR, it is important to note that significant

shortcomings in data for certain metrics result in significant barriers to rendering a clear picture

of the smart grid deployment landscape.

For this report and the 2009 SGSR, interviews were conducted with electricity service

providers to address these data gaps. This survey format should be retained for future reports

and be revised biennially to address any new material not included in previous interviews. As

more understanding regarding problematic metrics is acquired and new technologies and

information are brought to bear on the matter, questionnaires for these interviews can be

revised to form a more complete picture of smart grid deployment. Further, questionnaires for

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Smart Grid System Report | Page 81

these interviews should be developed and distributed earlier in the study schedule to maximize

the amount of time available to gather data and the level of information acquired through the

interview process. Finally, additional coordination with other smart grid information collection

activities (e.g., information resulting from smart grid ARRA investment grants &

demonstrations, Smart Grid Information Clearinghouse, Smart Grid Maturity Model) whose

products can be used in addressing data gaps should also be supported.

Besides reviewing the progress of developments for the metrics identified in this report,

future reports should consider addressing the following potential improvements:

• Further evaluation of stakeholder feedback, assessment of new developments, and

inclusion of additional stakeholder groups as necessary

• Further evaluation of privacy and data access policies, including consumer and third party

access to data

• Further investigation and analysis of the risks and challenges organizations and individuals

are facing with implementation of the various smart grid elements

• Incorporation and tracking of the consumer perspective, potentially through surveys, focus

groups, or analysis of consumer responses to smart grid elements reported by electricity

service providers or other groups (e.g., Smart Grid Consumer Collaborative)

• Review progress toward resolving smart grid challenges, identify new challenges, and

describe places where opportunities to advance smart grid concepts are occurring

Further recommendations specific to each metric can be found in Appendix A, which

presents the detailed results of investigations into the metrics. The end of each metric

description includes a subsection on recommendations for that metric. This report includes a

summary of those recommendations; future reports should consider a similar summation.

A final recommendation, particularly when considering this document in its entirety,

pertains to the overall objective of this report. The SGSR represents a biennial snapshot of

technology, business and policy advancements that at times change rapidly. Some themes that

are prominent in one report, such as the impacts of ARRA, may not pertain to future ones. The

characteristics and impacts of these landmark events in the evolution of a smart electric grid

should be well documented in future reports.

Department of Energy | February 2012

Smart Grid System Report | Page 83

6.0 Conclusions

The state of smart grid deployment covers a broad array of electricity system capabilities

and services enabled through pervasive communication and information technology, with the

objective of improving reliability, operating efficiency, resiliency to threats, and impact on the

environment. By collecting information from a workshop, interviews, and research of existing

smart grid literature and studies, this report attempts to present a balanced view of progress

across many fronts toward a smart grid.

Significant smart grid developments taking place since completion of the 2009 SGSR were

largely tied to the passage of ARRA, which designated $4.5 billion in awards for programs

described under Title XIII (111 USC 405). The impacts of ARRA on metrics in the SGSR include

the deployment of 877 PMUs [Metric 2 – Real-time System Operations Data Sharing],

investment in manufacturing facilities for EV batteries and components [Metric 8 – EVs and

PHEVs], $812.6 million in federal grant awards for AMI deployments with total project values

reaching over $2 billion, and $7.2 billion in funding to expand broadband access and adoption

[Metric 1-Dynamic Pricing, Metric 2-Real-time System Operations Data Sharing, Metric 12-

Advanced Meters]. Other significant developments affecting the deployment trends reported

in the 2010 SGSR include: expanded adoption of state renewable portfolio standards (there are

now 29 states with such standards), distributed resource interconnection policies being

implemented or expanded in 14 states since 2008, incentives for purchasing and owning EVs

and PHEVs now in place or planned in 21 states, and NIST releasing the first phase of a three-

phase plan that aims to align smart grid standards.

6.1 Progress towards Realizing the Characteristics of the

Modern Grid

To convey the present state of smart grid deployment, this report uses a set of six

characteristics derived from the NETL Modern Grid Initiative and documented in

“Characteristics of the Modern Grid” (Miller 2008). The metrics and analysis presented in this

report provide insights into progress toward these characteristics. Nearly all of the metrics

contribute information to understanding multiple characteristics. The main findings are

summarized below:

• Enabling Informed Participation by Customers: Supporting the bi-directional flow of

information and energy is a foundation for enabling participation through consumer

resources. AMI is receiving the most attention in terms of planning and investment.

Independent analyses of AMI penetration indicate deployments nationwide expanded to an

estimated 16 million in 2010, representing 10.7 percent of U.S. meters (Neichin and Cheng

2010, King 2010). In addition, state PUCs have approved an additional 34 million AMI

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deployments [Metric 12–Advanced Meters]. Smart grid technology, such as AMI, has

enabled implementation of dynamic pricing programs across the U.S. A FERC study found

that the number of service providers that reported offering some form of real-time pricing

tariff to enrolled customers increased from 60 in 2006 to 315 in 2008, and service providers

offering critical-peak pricing increased from 36 in 2006 to 88 in 2008 [Metric 1–Dynamic

Pricing] (FERC 2009a). Lastly, the amount of participating load based on grid conditions is

beginning to show a shift from traditional interruptible demand at industrial plants toward

demand-response programs that either allow an energy-service provider to perform direct

load control or provide financial incentives for customer-responsive demand at homes and

businesses; however, as of 2008, less than 2 percent of net summer generating capacity was

under load management programs [Metric 5–Load Participation Based on Grid Conditions].

• Accommodating All Generation & Storage Options: DER and interconnection standards to

accommodate generation capacity appear to be expanding in the U.S. Distributed

generation (carbon-based and renewable) and storage, although a small fraction (1 percent)

of total summer capacity, appear to be increasing rapidly [Metric 7–Grid-Connected

Distributed Generation]. Total DG capacity was 5,423 MW in 2004 but grew to 12,863 MW

in 2008, an increase of 137 percent (EIA 2010a). To expand favorability of interconnection

standards, EISA required interoperability policies to accommodate consumer distributed

resources, including DG, renewable generation, energy storage, energy efficiency, and

demand response (110 USC 1305). By June 2010, 39 states as well as Washington, D.C. and

Puerto Rico had adopted variations of an interconnection policy [Metric 3–Distributed

Resource Interconnection Policy]. Fourteen states have either imposed new policies or

expanded existing interconnection standards since 2008. The percentage of utilities with

standard resource-interconnection policies was based on whether the individual utilities

matched their state’s interconnection policies. Roughly 83.9 percent of utilities currently

have a standard resource-interconnection policy in place, compared with 61 percent in

2008 (EIA 2010b). Grid-connected renewable resources [Metric 21] represent more than

DG because they include wind farms and other large but decentralized sources of

generation. Currently, renewable resources excluding conventional hydro have reached

more than 3.5 percent of total generation and total output is expected to more than

quadruple by 2030.

• Enabling New Products, Services, & Markets: Investment in smart grid technologies has

continued to gain traction in recent years. Venture-capital funding secured by smart grid

startups was estimated at $194.1 million in 2007 and $414.0 million in 2009, representing a

two-year growth rate of over 113 percent (Fan 2008, Cleantech Group 2010). Deals totaling

more than $1.6 billion were identified during the 2000 through 2009 timeframe [Metric 20–

Venture Capital Investment]. Venture capital is only one source of R&D funding of smart

grid companies. Public and private agencies across the U.S. are increasingly investing in the

development of smart grid technologies. In addition to the $3.4 billion invested in smart

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grid matching-grant programs, ARRA also funded $327 million for research,

instrumentation, and laboratory infrastructure development (DOE 2009b). A smart grid also

supports the deployment of new vehicle technologies (e.g., EVs and PHEVs) [Metric 8].

Electric vehicle incentives are proliferating at the state level. Tax credits and other incentive

programs are either in place or proposed in 21 states. Recent research indicates that

ultimate penetration of EVs and PHEVs could reach as high as 30 to 60 percent by 2035

(Greene and Lin 2010). Regulatory rulings are becoming more inclined toward granting rate

recovery for smart grid investments [Metric 4–Regulatory Rate Recovery]. New products

and services from AMI programs have been significant beneficiaries of these rulings. A

smart grid includes consumer-oriented “smart” equipment, such as thermostats, space

heaters, clothes dryers, and water heaters that communicate to enable demand-side

participation. This equipment is just emerging, primarily in pilot programs [Metric 9–Grid-

Responsive Non-Generating Demand-Side Equipment].

• Providing the Power Quality for the Range of Needs: Not all customers have the same PQ

requirements, though traditionally these requirements and the costs to provide them have

been shared. The options for enhancing PQ with a smart grid cover a range of technologies

and service provider program approaches (e.g., PQ meters, storage devices, microgrids)

[Metric 17–Power Quality]. Various types of DG [Metric 7–Grid-Connected Distributed

Generation] and energy-storage equipment can improve PQ. These technologies, which are

connected to the grid, include backup generators, microturbines, combined heat and power

systems, solar panels, wind turbines and a wide range of energy storage technologies, such

as batteries, compressed air storage systems, flywheels, and ultracapacitors. Of the

projects funded by ARRA, there are 32 with a collective value of $620 million that combine

smart grid and energy storage functionalities (DOE 2009d). As mentioned earlier,

deployment of DER is trending upward, while microgrid parks [Metric 6–Load Served by

Microgrids] are just emerging and are mostly represented in pilot programs.

• Optimizing Asset Utilization & Operating Efficiently: Smart grid technologies are designed

to lower operation costs, reduce maintenance costs, and expand the flexibility of

operational control relative to the current grid system. This operational efficiency and

improved asset utilization is driven by advanced communication and information

technologies. Better monitoring and control technologies reduce the need for increased

generation capacity through demand response measures and energy efficiency. The

capacity factor for the U.S. [Metric 14–Capacity Factors] remained nearly unchanged from

2006 to 2008, making only a slight marginal improvement. On average, less than half of the

nation’s generation capacity is now used but more than 80 percent is used during summer

peaks (NERC 2009a). Smart grid techniques may be able to increase asset utilization, thus

increasing overall capacity factors. Data from T&D operators across the nation show a clear

trend of increasing T&D automation [Metric 11] and increasing investment in these

systems. From 2006 to 2008, more operators were planning increases in T&D automation

Department of Energy | February 2012

Smart Grid System Report | Page 86

investments (41 to 43 percent) than those decreasing future expenditures (5 to 11 percent)

(BusinessWire 2010). The last 50 years have seen relatively steady generation efficiency

rates with relative stability of coal and petroleum production and increases in natural gas.

T&D efficiency rates have also been relatively stable over the past two decades but grew

from 92.3 percent in 1995 to 94.1 percent in 2008 [Metric 15–Generation and T&D

Efficiencies] (EIA 2009).

• Operating Resiliently: Disturbances, Attacks, & Natural Disasters: The national averages

for reliability indices (outage duration and frequency measures SAIDI, SAIFI, and MAIFI)

appear to be trending upward [Metric 10–T&D System Reliability]. Smart-grid elements,

such as demand-side resource and DG participation in system operations are expected to

more elegantly respond to disturbances and emergencies. While non-generating demand-

side equipment [Metric 9] remains in a nascent stage of development, programmable,

communicating thermostats are a near-term success in the equipment category with

numerous installations on a pilot scale being launched around the U.S. For example, an

analysis performed by the CEC indicated that communicating, demand response

thermostats were cost effective in 15 of 16 studied climate zones. The total discounted

value of the thermostats ranged from as little as $71 to as high as $4,180 over 30 years for

single family dwellings. The savings for multi-family dwellings were not as significant with

only 6 of 16 climate zones deemed cost effective (California Utilities Statewide Codes and

Standards Team 2011). At the regional system operations level, advanced measurement

equipment is being deployed within the delivery infrastructure to support situational

awareness and enhance reliability coordination. Deployment numbers for one technology,

PMUs [Metric 2–Real-time System Operations Data Sharing], will grow from the current

network of 200 to 1,077 due to ARRA investments (Overholt 2010). Lastly, cyber security

challenges are beginning to be addressed with a more disciplined approach. In early 2008,

FERC directed NERC to tighten the standards to provide for external oversight of

classification of critical cyber assets and remove language allowing variable implementation

of the standards. The numbers of violations related to CIP have been on the decline in the

past two years, falling from as high as 306 in July 2008 to 54 in January of 2010 [Metric 18–

Cyber Security].

Different areas of the country have distinctions with regard to their generation resources,

their business economy, climate, topography, environmental concerns, and public policy.

These distinctions influence the picture for smart grid deployment in each region, provide

different incentives, and pose different obstacles to development.

6.2 Challenges to Smart Grid Deployments

Significant challenges still exist for bringing smart grid capabilities to fruition. Foremost

among these challenges is the value proposition and the capital required to purchase the new

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technologies envisioned for communicating information between end users, energy providers,

and distribution and transmission providers. Although progress has been made in the purchase

and installation of PMUs and AMI deployments, estimates for advanced metering capital still

range up to $40 billion despite the billions of dollars provided for smart grid funding in ARRA

(Faruqui and Sergici 2009). With ARRA investments made in PMUs, the grid will be approaching

the target level of PMUs to provide good coverage for grid monitoring (Overholt 2010).

Other areas of the grid including transmission, distribution, and software requirements will

also need capital investment to update the grid. Entrepreneurs that can find the appropriate

value propositions could lessen these significant capital requirements. However, with the rapid

changes in technology, energy mixes, and energy policy, the path to a smarter grid system

provides a degree of uncertainty that may not allow investors to clearly identify technologies

that will yield positive returns on investment. Yet, if legislators and regulators try to legislate or

regulate the smart grid’s development in order to provide certainty to investors, they could

inhibit technology innovations that might provide a more flexible and transparent energy

market. Nevertheless, allowing the grid to develop in a free market environment without some

guidance could put the energy grid and national interests at risk. Thus, the challenge is to

balance enabling innovation with providing some certainty to the markets.

Technical progress has recently been made in the area of reliability and cyber standards.

More importantly from a reliability perspective, however, progress has also been made in wide-

area situational awareness and control. Such progress will help to protect the grid from major

failures like the 2003 blackout of the Northeast but further progress is still needed. Significant

progress needs to be made in handling intermittent generation, including valuing storage

technologies and making them cost effective. Storage is one approach that could smooth

generation from renewable resources such as wind and solar. Studies indicate that when

variable renewable resources reach 20 percent penetration, they will be difficult and expensive

to manage (Pratt et al. 2010). The software and hardware required to allow this integration are

making significant progress, but the integration of these programs is still in the demonstration

stage. Lastly, regulations must focus on updating reliability standards, encouraging

interoperability, establishing favorable interconnection standards, and developing investment

recovery mechanisms for justified smart grid investments.

6.3 Recommendations for Future Reports

As the second in a series of biennial smart grid status reports required under EISA,

information gathered for this document should be used in conjunction with the 2009 SGSR to

form a framework and measurement baseline for future reports. Future reports may include

additional metrics, or it may be found necessary to alter existing metrics for technologies that

never reach full deployment levels or are ultimately rendered obsolete by other technologies.

Department of Energy | February 2012

Smart Grid System Report | Page 88

However, care should be taken to avoid the tendency to proliferate the number of metrics or

undertake extensive changes from one report to the next. In preparing this report, the

research team followed the recommendation of the 2009 SGSR in defining and reviewing

metrics. As a result, a small number of changes were made to the SGSR metrics as outlined

previously.

Due to the early stages of development and deployment of some systems, many

technologies and standards evaluated in this report lack concrete definitions, causing

complications and potential discrepancies in data measurement. Future studies should

consider adjustments to these definitions based on input provided by industry and regulatory

bodies. In addition, there is absent or incomplete data for some of the metrics evaluated in this

report. A primary focus of future reports should be closing these data gaps through interviews

with electricity service providers and collaborations with private collectors of energy sector

data.

Besides reviewing the progress of developments for the metrics identified in this report,

future reports should consider addressing the following potential improvements:

• Further evaluate stakeholder feedback through assessment of new developments and

inclusion of additional stakeholder groups as necessary

• Incorporate and track the consumer perspective

• Review progress toward resolving smart grid challenges, identify new challenges, and

describe places where opportunities to advance smart grid concepts are occurring

Further recommendations specific to each metric can be found in Appendix A, which

presents the detailed results of investigations into the metrics.

Department of Energy | February 2012

Smart Grid System Report | Page 89

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